LLM System Design and Mannequin Choice – O’Reilly

Selecting the best LLM has turn out to be a full-time job. New fashions seem nearly each day, every providing completely different capabilities, costs, and quirks, from reasoning strengths to value effectivity to code technology. This competitors creates robust incentives for AI labs to carve out a distinct segment and offers new startups room to emerge, leading to a fragmented panorama the place one mannequin could excel at reasoning, one other at code, and a 3rd at value effectivity.

AI, in a single sense, is getting cheaper quicker than any earlier know-how, no less than per unit of intelligence. For instance, enter tokens for Gemini 2.5 Flash-Lite are roughly 600 occasions cheaper than what OpenAI’s GPT-3 (davinci-002) value in August 2022, whereas outperforming it on each metric. On the similar time, entry to frontier capabilities can be turning into dearer than ever. The reason being easy: we are able to now pay straight for extra functionality, which has led to the rise of $300+ per thirty days Professional subscription tiers.

At present, any developer can run succesful open-weight fashions domestically for negligible marginal value utilizing instruments like Ollama. On the similar time, enterprise techniques can expertise sharp value will increase, relying on the mannequin dimension (variety of parameters, corresponding to 3 billion, 70 billion, and even within the trillions), the variety of inner processing steps, and the amount of enter information. For builders, these are central system design selections that straight have an effect on feasibility and price construction. For finish customers, this complexity explains why a primary subscription differs a lot from a premium plan with larger limits on superior fashions.

The alternatives you make in these broader growth selections additionally decide which LLM and inference settings are optimum on your use case.

At In direction of AI, we work throughout the LLM stack, constructing purposes, designing enterprise techniques, and providing on-line programs (together with one on O’Reilly), customized company coaching, and LLM growth consultancy. In our expertise, mannequin choice and system design have turn out to be central to getting significant outcomes from these instruments. A lot of that, in flip, relies on the place at present’s fashions are gaining their capabilities. Whereas scale nonetheless performs a job, current progress has come from a broader combine of things, together with training-data high quality, post-training strategies, and particularly how fashions are used at inference time.

The Shifting Foundations of Mannequin Functionality

Whereas early positive aspects in LLM efficiency tracked intently with will increase in pretraining compute, bigger datasets, larger fashions, and extra coaching steps, this method now yields diminishing returns.

Current enhancements come from a broader mixture of methods. Pretraining-data high quality has turn out to be simply as essential as amount, with higher filtering and AI-generated artificial information contributing to stronger fashions. Architectural effectivity, just like the improvements launched by DeepSeek, has began to shut the hole between dimension and functionality. And post-training methods, particularly instruction tuning and reinforcement studying from human or AI suggestions (RLHF/RLAIF), have made fashions extra aligned, controllable, and responsive in apply.

The extra elementary shift, nonetheless, is going on at inference time. Since late 2024, with fashions like OpenAI’s o1, we’ve entered a brand new part the place fashions can commerce compute for reasoning on demand. Somewhat than relying solely on what was baked in throughout coaching, they will now “assume tougher” at runtime, working extra inner steps, exploring different solutions, or chaining ideas earlier than responding. This opens up new functionality ceilings, but in addition introduces new value dynamics.

These assorted enchancment methods have led to a transparent divergence amongst AI labs and fashions, a fast growth in mannequin alternative, and in some circumstances, an explosion in mannequin utilization prices.

The Fashionable Value Explosion: How Inference Scaling Modified the Sport

Inference-time compute scaling has launched a brand new dynamic in LLM system design: We’ve gone from a single lever mannequin dimension, to no less than 4 distinct methods to commerce value for functionality at runtime. The result’s a widening hole in inference value throughout fashions and use circumstances, generally by elements of 10,000x or extra.

Bigger fashions (dimension scaling): The obvious lever is sheer mannequin dimension. Frontier LLMs, like GPT-4.5, usually constructed with combination of consultants (MoE) architectures, can have enter token prices 750 occasions larger than streamlined fashions like Gemini Flash-Lite. Bigger parameter counts imply extra compute per token, particularly when a number of consultants are energetic per question.

Sequence scaling (“pondering tokens”): Newer “reasoning” LLMs carry out extra inner computational steps, or an extended chain of thought, earlier than producing their remaining reply. For instance, OpenAI’s o1 used ~30x extra compute than GPT-4o on common, and infrequently 5x extra output tokens per activity. Agentic techniques introduce a further methodology of sequence scaling and an additional layer of value multiplication. As these brokers assume, plan, act, reassess, plan, act, and so forth, they usually make many LLM steps in a loop, every incurring further value.

Parallel scaling: Right here, the system runs a number of mannequin situations on the identical activity after which mechanically selects the most effective output by way of automated strategies, corresponding to majority voting (which assumes the commonest reply is probably going appropriate) or self-confidence scores (the place the mannequin output claiming the best confidence in its response is taken as the most effective). The o3-pro mannequin possible runs 5–10x parallel situations over o3. This multiplies the fee by the variety of parallel makes an attempt (with some nuance).

Enter context scaling: In RAG pipelines, the variety of retrieved chunks and their dimension straight affect enter token prices and the LLM’s means to synthesize a great reply. Extra context can usually enhance outcomes, however this comes at a better value and potential latency. Context isn’t free; it’s one other dimension of scaling that builders should price range for.

Taken collectively, these 4 elements signify a elementary shift in how mannequin value scales. For builders designing techniques for high-value issues, 10,000x to 1,000,000x variations in API prices to unravel an issue based mostly on architectural selections are actually lifelike prospects. Reasoning LLMs, though solely outstanding for about 9 months, reversed the development of declining entry prices to the easiest fashions. This transforms the choice from “Which LLM ought to I exploit?” to incorporate “How a lot reasoning do I wish to pay for?”

This shift modifications how we take into consideration choice. Selecting an LLM is now not about chasing the best benchmark rating; it’s about discovering the steadiness level the place functionality, latency, and price align along with your use case.

Core Mannequin Choice Standards

When selecting a mannequin we discover you will need to first clearly establish your use case and the minimal core AI capabilities and attributes wanted to ship it.

A standard first step is to try customary benchmark scores (for instance LiveBench, MMLU-Professional, SWE-Bench). These benchmarks are a helpful place to begin, however some fashions are tuned on benchmark information, and real-world efficiency on duties which might be truly related to you’ll usually range. Filtering benchmark checks and scores by your business and activity class is a precious step right here. An LLM optimized for software program growth would possibly carry out poorly in artistic writing or vice versa. The match between a mannequin’s coaching focus and your utility area can outweigh general-purpose benchmarks.

Leaderboards like LMArena and Synthetic Evaluation supply broader human‑choice comparisons however nonetheless don’t exchange customized real-world testing. It helps to have a set of your individual instance questions or duties at hand to check out a brand new mannequin for your self and see the way it performs. This could embrace a mixture of straightforward duties to ascertain a baseline and difficult edge circumstances the place it’s straightforward for a mannequin to make errors.

As you progress past advert hoc testing, for any critical growth effort, customized evaluations are non-negotiable. They have to be tailor-made to your use case and the varieties of issues you remedy. That is the one method to really know if a mannequin, or a change to your system, is genuinely bettering issues for your customers and your particular enterprise objectives.

Listed below are some core elements we think about:

Multimodality is rising as a serious differentiator. Fashions like GPT-4o and Gemini can deal with not simply textual content but in addition pictures, audio, and in some circumstances video, unlocking purposes that pure textual content fashions can’t assist.

Context window and efficient context window utilization are additionally key: What number of tokens or paperwork can the mannequin course of and the way a lot of that marketed context window can the LLM truly use successfully with out efficiency degradation relative to duties that use much less context?

Latency is very essential for interactive purposes. Usually, smaller or cheaper fashions have a tendency to reply quicker, whereas reasoning-heavy fashions introduce delays resulting from deeper inner computation.

Reasoning is the power to scale inference-time compute and carry out multistep problem-solving, planning, or deep evaluation.

Privateness and safety are sometimes key concerns right here. For instance, if you wish to maintain your mental property non-public, it’s essential to use a mannequin that received’t practice in your inputs, which frequently factors towards self-hosted or particular enterprise-grade API options.

Trustworthiness can be turning into essential and might come right down to the status and observe report of the AI lab. A mannequin that produces erratic, biased, or reputationally damaging outputs is a legal responsibility, no matter its benchmark scores. For example, Grok has had well-publicized points with its alignment. Even when such points are supposedly fastened, it creates a lingering query of belief: How can one make sure it received’t behave equally sooner or later?

Moreover, the information cutoff date additionally issues whether it is for use in a fast-moving discipline.

After understanding if a mannequin meets your minimal functionality, the following determination is commonly on optimizing trade-offs amongst value, reliability, safety, and latency. A key rule of thumb we discover helpful right here: If the reliability achieve from a dearer mannequin or extra inference time saves extra of your or your customers’ time (valued when it comes to pay) than the mannequin prices, going with the bigger mannequin is an effective determination!

The Professionals and Cons of Open-Weight and Closed-API LLMs

The rise of more and more aggressive open-weight LLMs, corresponding to Meta’s Llama sequence, Mistral, DeepSeek, Gemma, Qwen, and now OpenAI’s GPT-OSS has added a essential dimension to the mannequin choice panorama. Momentum behind this open ecosystem surged with the discharge of DeepSeek’s R1 reasoning mannequin, aggressive with OpenAI’s o1 however priced at roughly 30x decrease API prices. This sparked debate round effectivity versus scale and intensified the broader AI rivalry between China and the US. Reactions ranged from “OpenAI and Nvidia are out of date” to “DeepSeek’s prices have to be fabricated,” however no matter hype, the discharge was a milestone. It confirmed that architectural innovation, not simply scale, may ship frontier-level efficiency with far higher value effectivity.

This open-model offensive has continued with robust contributions from different Chinese language labs like Alibaba (Qwen), Kimi, and Tencent (Hunyuan), and has put aggressive stress on Meta after its open-weight Llama fashions fell behind. China’s current management in open-weight LLMs has raised new safety/IP points with some US- and European-based organizations, although we be aware accessing these mannequin weights and working the mannequin by yourself infrastructure doesn’t require sending information to China.

This brings us again to the professionals and cons of open weights. Whereas closed-API LLMs nonetheless lead on the frontier of functionality, the first benefit of open-weight fashions is fast and inexpensive native testing, unparalleled flexibility, and elevated information safety when run internally. Organizations may carry out full fine-tuning, adapting the mannequin’s core weights and behaviors to their particular area, language, and duties. Open fashions additionally present stability and predictability—you management the model you deploy, insulating your manufacturing techniques from sudden modifications or degradations that may generally happen with unannounced updates to proprietary API-based fashions.

Public closed-model APIs from main suppliers profit from immense economies of scale and extremely optimized GPU utilization by batching requests from hundreds of customers, an effectivity that’s tough for a single group to duplicate. This usually implies that utilizing a closed-source API may be cheaper per inference than self-hosting an open mannequin. Safety and compliance are additionally extra nuanced than they first seem. Whereas some organizations should use self-hosted fashions to simplify compliance with laws like GDPR by maintaining information solely inside their very own perimeter, this locations the complete burden of securing the infrastructure on the inner staff—a fancy and costly enterprise. Prime API suppliers additionally usually supply devoted situations, non-public cloud endpoints, and contractual agreements that may assure information residency, zero-logging, and meet stringent regulatory requirements. The selection, due to this fact, shouldn’t be a easy open-versus-closed binary.

The boundary between open and closed fashions can be turning into more and more blurred. Open-weight fashions are more and more provided by way of API by third-party LLM inference platforms, combining the pliability of open fashions with the simplicity of hosted entry. This hybrid method usually strikes a sensible steadiness between management and operational complexity.

Main Closed LLMs

Beneath, we current some key prices and metrics for main closed-source fashions obtainable by way of API. Many of those fashions have further complexity and assorted pricing together with choices for quick modes, pondering modes, context caching, and longer context.

We current the most recent LiveBench benchmark rating for every mannequin as one measure for comparability. LiveBench is a constantly up to date benchmark designed to supply a “contamination-free” analysis of huge language fashions by frequently releasing new questions with goal, verifiable solutions. It scores fashions out of 100 on a various set of difficult duties, with a big concentrate on capabilities like reasoning, coding, and information evaluation. The same LiveBench scores between GPT-4.5 and a couple of.5 Flash-Lite, regardless of 750x enter token value variation, highlights each that smaller fashions are actually very succesful but in addition that not all capabilities are captured in a single benchmark!

Main open-weight LLMs

Beneath, we additionally current key prices, the LiveBench benchmark rating, and context size for main open-weight fashions obtainable by way of API. We evaluate hosted variations of those fashions for simple comparability. Completely different API suppliers could select to host open-weight fashions with completely different ranges of quantization, completely different context lengths, and completely different pricing, so efficiency can range between suppliers.

Whether or not hosted or self-deployed, choosing a mannequin solely solves a part of the issue. In apply, a lot of the complexity and alternative lies in how that mannequin is used: the way it’s prompted, prolonged, fine-tuned, or embedded inside a broader workflow. These system-level selections usually have a higher influence on efficiency and price than the mannequin alternative itself.

A Sensible Information to Designing an LLM System

Merely selecting the most important or latest LLM isn’t the optimum technique. A more practical method begins with a deep understanding of the developer’s toolkit: realizing which method to use to which drawback to attain the specified functionality and reliability with out pointless value. That is all a part of the fixed “march of nines” as you develop LLM techniques modularly to unravel for extra reliability and functionality. There’s a have to prioritize the simplest wins that ship tangible worth earlier than investing in additional incremental and infrequently expensive accuracy enhancements. The truth will at all times range on a case-by-case foundation, however here’s a fast information to navigating this course of.

Step 1: Open Versus Closed?

That is usually your first determination.

• Go along with a closed-API mannequin (e.g., from OpenAI, Google, Anthropic) if: Your precedence is accessing absolutely the state-of-the-art fashions with most simplicity.
• Go along with an open-weight mannequin (e.g., Llama, Mistral, Qwen, DeepSeek) if:
  • Knowledge safety and compliance are paramount: If it’s good to assure that delicate information by no means leaves your individual infrastructure.
  • You want deep customization and management: In case your objective is to fine-tune a mannequin on proprietary information and to create a specialised skilled that you simply management fully.

If you happen to went open, what are you able to realistically run? Your personal GPU infrastructure is a tough constraint. Assess your cluster dimension and reminiscence to find out should you can effectively run a big, main 1 trillion+ parameter MoE mannequin, corresponding to Kimi K2, or in case you are higher served by a medium-size mannequin corresponding to Gemma 3 27B or a a lot smaller mannequin like Gemma 3n that may even run on cell.

Step 2: Gauging the Want for Reasoning

Does your activity require the mannequin to easily blast out a response, or does it have to assume first?

• Reasoning: For duties that contain advanced, multistep problem-solving, brainstorming, strategic planning, intricate code technology, or deep evaluation, you want a devoted reasoning mannequin corresponding to o3, Gemini 2.5 Professional, DeepSeek R1, or Claude 4. In some circumstances these fashions can be utilized in high-reasoning mode, which inspires the mannequin to assume for longer earlier than responding.
• No reasoning: For easy duties like easy Q&A, summarization of a single doc, information extraction, or classification, a strong reasoning mannequin is overkill.
• The center floor: For duties requiring average reasoning, corresponding to producing a structured report from a number of information factors or performing primary information evaluation at scale, a “mini” reasoning mannequin, like OpenAI’s o4-mini or Gemini Flash 2.5, gives a steadiness of functionality and price.

Step 3: Pinpointing Key Mannequin Attributes

Past common intelligence and reasoning, trendy LLMs are specialists. Your alternative needs to be guided by the precise attributes and “superpowers” your utility wants.

• Prioritize accuracy over value for high-value duties the place errors are expensive or the place a human skilled’s time is being saved. o3-pro is a standout mannequin right here and it might probably even be used as a truth checker to meticulously examine the small print of an earlier LLM output.
• Prioritize velocity and price over accuracy: For user-facing, real-time purposes like chatbots or high-volume, low-value duties like easy information categorization, latency and price are paramount. Select a hyper-efficient “flash” or “mini” mannequin corresponding to Gemini 2.5 Flash-Lite. Qwen3-235B fashions may also be a terrific choice right here however are too advanced to inference your self.
• Do you want a deep, long-context researcher? For duties that require synthesizing data from huge paperwork, complete codebases, or in depth authorized contracts, a mannequin with an unlimited and extremely efficient context window is essential. Gemini 2.5 Professional excels right here.
• Is multimodality important? In case your utility wants to know or generate pictures, course of audio in actual time, or analyze video, your alternative narrows to fashions like GPT-4o or the Gemini household. For one-shot YouTube video processing, Gemini is the standout.
• Is it a code-specific activity? Whereas many fashions can code, some are explicitly tuned for it. Within the open world, Codestral and Gemma do an honest job. However Claude has received hearts and minds, no less than for now.
• Do you want stay, agentic internet search? For answering questions on present occasions or subjects past the mannequin’s information cutoff, think about a mannequin with a built-in, dependable internet search, corresponding to o3.
• Do you want advanced dialogue and emotional nuance? GPT-4.5, Kimi K2, Claude Opus 4.0, or Grok 4 do a terrific job.

Step 4: Prompting, Then RAG, Then Analysis

Earlier than you dive into extra advanced and expensive growth, at all times see how far you may get with the best methods. It is a path of escalating complexity. Mannequin alternative for RAG pipelines is commonly centered round latency for finish customers, however just lately extra advanced agentic RAG workflows or long-context RAG duties require reasoning fashions or longer context capabilities.

1. Immediate engineering first: Your first step is at all times to maximise the mannequin’s inherent capabilities by clear, well-structured prompting. Typically, a greater immediate with a extra succesful mannequin is all you want.
2. Transfer to retrieval-augmented technology (RAG): In case your mannequin’s limitation is a scarcity of particular, non-public, or up-to-date information, RAG is the following logical step. That is the most effective method for decreasing hallucinations, offering solutions based mostly on proprietary paperwork, and guaranteeing responses are present. Nonetheless, RAG shouldn’t be a panacea. Its effectiveness is solely depending on the standard and freshness of your dataset, and constructing a retrieval system that constantly finds and makes use of the most related data is a big engineering problem. RAG additionally comes with many related selections, corresponding to the amount of knowledge to retrieve and feed into the mannequin’s context window, and simply how a lot use you make of long-context capabilities and context caching.
3. Iterate with superior RAG: To push efficiency, you’ll need to implement extra superior methods like hybrid search (combining key phrase and vector search), re-ranking retrieved outcomes for relevance, and question transformation.
4. Construct customized analysis: Guarantee iterations in your system design, additions of recent superior RAG methods, or updates to the most recent mannequin are at all times transferring progress ahead in your key metrics!

Step 5: Positive-Tune or Distill for Deep Specialization

If the mannequin’s core conduct—not its information—remains to be the issue, then it’s time to contemplate fine-tuning. Positive-tuning is a big enterprise that requires a high-quality dataset, engineering effort, and computational sources. Nonetheless, it might probably allow a smaller, cheaper open-weight mannequin to outperform a large generalist mannequin on a particular, slim activity, making it a strong software for optimization and specialization.

• Positive-tuning is for altering conduct, not including information. Use it to show a mannequin a particular talent, model, or format. For instance:
  • To reliably output information in a fancy, structured format like particular JSON or XML schemas.
  • To grasp the distinctive vocabulary and nuances of a extremely specialised area (e.g., authorized, medical).
  • Some closed-source fashions can be found for fine-tuning by way of API corresponding to Gemini 2.5 Flash and numerous OpenAI fashions. Bigger fashions are usually not obtainable.
  • In open-weight fashions, Llama 3.3 70B and Qwen 70B are fine-tuning staples. The method is extra advanced to fine-tune an open-weight mannequin your self.
• Mannequin distillation may function a production-focused optimization step. In its easiest type, this consists of producing artificial information from bigger fashions to create fine-tuning datasets to enhance the capabilities of smaller fashions.
• Reinforcement fine-tuning (RFT) for problem-solving accuracy
  As a substitute of simply imitating appropriate solutions, the mannequin learns by trial, error, and correction. It’s rewarded for getting solutions proper and penalized for getting them incorrect.
  • Use RFT to: Create a real “skilled mannequin” that excels at advanced duties with objectively appropriate outcomes.
  • The benefit: RFT is extremely data-efficient, usually requiring just a few dozen high-quality examples to attain vital efficiency positive aspects.
  • The catch: RFT requires a dependable, automated “grader” to supply the reward sign. Designing this grader is a essential engineering problem.

Step 6: Orchestrated Workflows Versus Autonomous Brokers

The essential determination right here is how a lot freedom to grant. Autonomous brokers are additionally extra more likely to want dearer reasoning fashions with higher ranges of inference scaling. Parallel inference scaling strategies with a number of brokers are additionally starting to ship nice outcomes. Small errors can accumulate and multiply throughout many successive agentic steps so the funding in a stronger extra succesful mannequin could make all of the distinction in constructing a usable product.

• Select an orchestrated workflow for predictable duties
  You design a particular, usually linear, sequence of steps, and the LLM acts as a strong element at a number of of these steps.
  • Use when: You might be automating a recognized, repeatable enterprise course of (e.g., processing a buyer assist ticket, producing a month-to-month monetary abstract). The objective is reliability, predictability, and management.
  • Profit: You keep full management over the method, guaranteeing consistency and managing prices successfully as a result of the quantity and kind of LLM calls are predefined.
• Construct hybrid pipelines: Typically, the most effective outcomes will come from combining many LLMs, open and closed, inside a pipeline.
  • This implies utilizing completely different LLMs for various phases of a workflow: a quick, low cost LLM for preliminary question routing; a specialised LLM for a particular subtask; a strong reasoning LLM for advanced planning; and maybe one other LLM for verification or refinement.
  • At In direction of AI, we regularly have 2-3 completely different LLMs from completely different corporations in an LLM pipeline.
• Select an autonomous agent for open-ended issues. You give the LLM a high-level objective, a set of instruments (e.g., APIs, databases, code interpreters), and the autonomy to determine the steps to attain that objective.
  • Use when: The trail to the answer is unknown and requires dynamic problem-solving, exploration, or analysis (e.g., debugging a fancy software program subject, performing deep market evaluation, planning a multistage challenge).
  • The essential danger—runaway prices: An agent that will get caught in a loop, makes poor selections, or explores inefficient paths can quickly accumulate monumental API prices. Implementing strict guardrails is essential:
    • Funds limits: Set exhausting caps on the fee per activity.
    • Step counters: Restrict the full variety of “ideas” or “actions” an agent can take.
    • Human-in-the-loop: Require human approval for doubtlessly costly or irreversible actions.
  • Gemini 2.5 Professional and o3 are our favorite closed-API fashions for agent pipelines, whereas in open-weight fashions we like Kimi K2.

Working by these steps helps translate a imprecise drawback right into a concrete implementation plan, one which’s grounded in clear trade-offs and tailor-made to your wants. This structured method usually yields techniques that aren’t solely extra succesful and dependable but in addition far more practical for particular duties than a general-purpose chatbot ever may very well be.

Conclusion

The open-versus-closed race offers us fast entry to robust LLMs but in addition creates complexity. Choosing and deploying them calls for each engineering self-discipline and financial readability.

Growing within the LLM ecosystem calls for a brand new degree of engineering self-discipline and eager financial consciousness. No single LLM is a cure-all. A sensible, evolving toolkit is crucial, however realizing which software to tug out for which job is the actual artwork. The problem isn’t simply selecting a mannequin from an inventory; it’s about architecting an answer. This requires a scientific method, transferring from high-level strategic selections about information and safety right down to the granular, technical selections of growth and implementation.

The success of specialised “LLM wrapper” purposes like Anyscale/Cursor for coding or Perplexity for search, a few of which are actually valued at over $10 billion, underscores the immense worth on this tailor-made method. These purposes aren’t simply skinny wrappers; they’re refined techniques that leverage basis LLMs however add vital worth by customized workflows, fine-tuning, information integration, and consumer expertise design.

Finally, success hinges on knowledgeable pragmatism. Builders and organizations want a pointy understanding of their drawback house and a agency grasp of how value scales throughout mannequin alternative, sequence and parallel reasoning, context utilization, and agentic conduct. Above all, customized analysis is non-negotiable as a result of your use case, not a benchmark, is the one customary that really issues.