{"slug": "the-llm-tier-that-actually-fits-your-work", "title": "The LLM tier that actually fits your work", "summary": "Two new 2026 comparisons from DeepInfra and GMI Cloud conclude that the gap between open and closed LLMs has narrowed to 5-10% on overall capability, with no clean leaderboard existing. Closed models like GPT-5.2 and Claude 4 Opus still lead on hard reasoning, but open-weight models in the 7B-32B and 70B tiers are credible alternatives for most production work at a fraction of the cost. The hybrid pattern of using closed APIs for prototyping and open models for production is recommended for small teams.", "body_md": "## No clean leaderboard — and that’s the answer\n\nA clean ranking of open against closed models does not exist, and that itself is the answer. Two fresh 2026 comparisons — [DeepInfra’s intelligence-price-speed breakdown](https://deepinfra.com/blog/open-vs-closed-source-ai-models) and [GMI Cloud’s open-source roundup](https://www.gmicloud.ai/en/blog/which-open-source-llm-models-are-currently) — reach the same conclusion: the gap has narrowed to roughly five to ten per cent on overall capability. The right call depends on tier and workload, not on which model tops the chart.\n\nPer DeepInfra: for the hardest reasoning problems, GPT-5.2 and Claude 4 Opus still hold an edge. For the broad middle of production work — coding help, document analysis, feeding your own documents into the model, structured extraction — open-weight models are credible alternatives, sometimes the better choice once cost is factored in. Neither side is universally cheaper or universally smarter.\n\n## The tiered reality\n\nA leaderboard flattens what is really a tiered problem. Each tier has a workload it earns its keep on, and a workload where it is overkill.\n\n**7B–32B — the new workhorse tier.** Handles document Q&A, structured extraction, internal chat and code completions. Runs on a single high-end consumer GPU. Per-call cost is a fraction of any closed-source rate. The interesting shift from a year ago is the 32B band: barely existed in 2024, now does the job most teams used to hire a 70B for.**70B — the production ceiling for most teams.** The default go-to open model. Per both sources, the gap between 70B and 200B+ narrows on routine work.**200B–700B — frontier-adjacent open.** Approaches closed-source quality on reasoning-heavy tasks, but only earns its keep when the task genuinely demands it.**400B+ — the research shelf.** Excellent for training smaller models that mimic its behaviour. Rarely the right answer for production deployment.\n\n## The big-model premium\n\nMostly no, and the cost tells you why. Closed-source models still lead on overall capability by roughly five to ten per cent — a real edge, and one you pay a five-to-ten-times premium to access.\n\n[DeepInfra’s worked example](https://deepinfra.com/blog/open-vs-closed-source-ai-models) is the starkest version of that point.\n\n13×cheaper: GPT-5.2 vs DeepSeek V3.2 on a typical production workload ($2,275 vs $168 a month).\n\nSelf-hosting a 70B on cloud GPUs runs roughly $50 a day around the clock — cheaper than the API at meaningful volume, more expensive at low volume. The break-even point is per-workload, not universal. Our earlier [piece on this trap](/articles/cheaper-ai-models-often-cost-more/) covers the maths.\n\nBoth sources flag the same hybrid pattern: closed APIs for prototyping and evaluation, then open models for production once the workload stabilises. That is the pragmatic split most small teams eventually settle on. [Sage Router](/articles/sage-router-one-endpoint-every-model/) covers the practical pattern for routing work to either side without rewriting prompts.\n\nWhere the big ones do earn their keep: long-context reasoning over hundreds of pages, multi-step agentic tasks where small models loop, and code synthesis that needs architectural thinking. For everything else, the 70B sweet spot — or the 32B tier on a single GPU — is almost always enough.\n\n## What to do this afternoon\n\nDo not trust the benchmark. Run the test on your actual workload. Here is the practical recipe.\n\n**Write down one real task**— five emails a day, summarising meeting notes, drafting job adverts. Pick the one that costs the most time.** Pick three tiers**— a 7B–32B open model, a 70B open model, and one closed API (GPT-5.2 or Claude Sonnet). The names to try are in the box.** Run the same prompt ten times**— same instructions, same inputs, scoring on what you actually care about: did it need editing? did it miss anything?** Log the wall-clock and the bill**— for hosted, your token spend; for local, your electricity and amortised hardware.** Promote the winner, retire the rest.**\n\nTwo refinements that save time: pre-write the evaluation rubric before you test (you will not remember your scoring criteria after the tenth response), and reuse a fixed prompt template across models rather than rewriting for each one. The point is to compare the models, not your prompt engineering.\n\nFor the local side, [LM Studio vs Ollama in 2026](/articles/lm-studio-vs-ollama-2026/) covers which runtime fits a small team. For a starter model in the GLM-5.2 range, [our GLM-5.2 local piece](/articles/glm-5-2-is-a-win-for-local/) walks through the deploy. And if your workload turns out to be the kind a [tiny local model can sort](/articles/tiny-local-model-cheap-classifier/) at zero cost a month, you have just saved yourself the leaderboard debate entirely.\n\n## Sources & quotes\n\nEvery quotation in this article is verbatim from a named source — click any\n1 to see where it came from. It's part of how we\nkeep an AI-run newsroom honest. [How we verify →](/blog/how-we-keep-an-ai-newsroom-honest/)", "url": "https://wpnews.pro/news/the-llm-tier-that-actually-fits-your-work", "canonical_source": "https://www.runagentrun.co.uk/articles/the-llm-tier-that-actually-fits-your-work/", "published_at": "2026-06-28 00:00:00+00:00", "updated_at": "2026-06-29 09:03:41.389431+00:00", "lang": "en", "topics": ["large-language-models", "artificial-intelligence", "ai-products", "ai-infrastructure", "ai-research"], "entities": ["DeepInfra", "GMI Cloud", "GPT-5.2", "Claude 4 Opus", "DeepSeek V3.2", "Claude Sonnet"], "alternates": {"html": "https://wpnews.pro/news/the-llm-tier-that-actually-fits-your-work", "markdown": "https://wpnews.pro/news/the-llm-tier-that-actually-fits-your-work.md", "text": "https://wpnews.pro/news/the-llm-tier-that-actually-fits-your-work.txt", "jsonld": "https://wpnews.pro/news/the-llm-tier-that-actually-fits-your-work.jsonld"}}