Ranked #1 on the LMRank leaderboard with a 1M-token context window and mid-tier input pricing ($10/1M in, $50/1M out) for a frontier model.
Use it if:
You want the strongest coding model available and bill by usage: large refactors, multi-step agent work, or million-token codebases where getting it right the first time matters more than token cost.
Skip it if:
You are running very high volumes on a budget — frontier pricing adds up, and cheaper picks below are close for everyday tasks.
Buying through our links may earn LMRank a commission at no cost to you — it never affects our picks. See our methodology.
From OpenAI’s latest GPT-5.6 family at $1/1M input and $6/1M output with a 1M-token context — a tenth of the top pick’s input price for everyday coding work.
Use it if:
You ship code all day and want frontier-family quality at a fraction of flagship pricing — daily driver for IDE assistants, CI review bots, and high-volume generation.
Skip it if:
You need the absolute strongest reasoning on gnarly refactors; step up to the top pick for those.
Buying through our links may earn LMRank a commission at no cost to you — it never affects our picks. See our methodology.
Open-weight family, 1M-token context, at $0.10/1M input and $0.20/1M output — roughly 100x cheaper per token than the top pick for routine coding tasks.
Use it if:
Cost is the constraint: bulk batch jobs, experimentation, hobby projects, or self-serve products where margin per request matters.
Skip it if:
You need top-tier agentic reliability or deep reasoning — it is a lightweight model and expects more supervision than the picks above.
Buying through our links may earn LMRank a commission at no cost to you — it never affects our picks. See our methodology.
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How we chose
We start from the LMRank leaderboard — public benchmarks (including SWE-bench), current pricing, and context windows — then narrow to models that make sense as an actual purchase decision. Every pick has a job: the top pick wins on capability, the value pick wins on capability per dollar, and the budget pick wins when cost is the constraint. Rankings and prices refresh continuously from public data sources; the verdicts here are editorial and re-checked whenever a pick changes.
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