Best Value LLM for Coding in 2026 — Cost-Adjusted Rankings
As of October 29, 2026, the top model among value LLM for coding on the BenchLM leaderboard is Ministral 3 3B with a score of 281.6.
Bottom line: value here is coding score per output dollar, so check each row's raw score before you shortlist it.
Full Rankings (96 ranked, 1 tracked)
Open weights (mistralai/Mistral-Large-4-1T-A52B) under the Mistral Ecosystem Open-Weight license, a modified MIT license; businesses running a Model-as-a-Service offering need a separate commercial license. API ID mistral-large-4-0, with Low, Medium and High (default) reasoning modes.
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tracked
Key Takeaways
The best value model is Ministral 3 3B by Mistral with a provisional Score/$ ratio of 281.6 (score: 28.2, output: $0.1/1M tokens).
The best open-weight model is Ministral 3 3B at position #1.
96 models are ranked here; 1 tracked model is listed without a position.
Score in Context
What these scores mean
Value scores divide the weighted coding score by output token price (per 1M tokens). Higher means more capability per dollar. Models with no listed price are excluded.
Known limitations
Value rankings favor cheap models even if absolute performance is modest. A model scoring half as well at one-tenth the price wins on value — but may not meet your quality bar. Always check raw scores alongside value rankings.
About this ranking
Ranking data as of October 29, 2026
Raw benchmark scores only tell half the story. This ranking divides each model's weighted coding score by its output token price, surfacing models that deliver the most coding capability per dollar spent. A model scoring 70 at $1/1M tokens outranks one scoring 80 at $15/1M tokens here — because for the same budget you get far more coding work done. Use this alongside the standard coding leaderboard to find the sweet spot between performance and cost for your coding workflows.
For a browser interface or a small web app, use the frontend model shortlist and test the generated UI before accepting it.
Unless noted otherwise, ranking surfaces on this page use BenchLM’s provisional leaderboard lane rather than the stricter sourced-only verified leaderboard.
Ministral 3 3B leads this ranking with a score of 281.6, followed by MiMo-V2.6-Flash (198.82) and Ministral 3 8B (193.67). There is a significant gap between the leading models and the rest of the field.
The best open-weight option is Ministral 3 3B (ranked #1 with a score of 281.6). Open-weight models are highly competitive in this category — self-hosting is a viable alternative to proprietary APIs.
This ranking uses provisional weighted averages across the scoring benchmarks in coding. For detailed model profiles, click any model name above. To compare two specific models head-to-head, use the "vs #" links.
Questions
What is the best LLM for coding right now?
For raw coding capability, use the live coding leaderboard — it recomputes the order from calibrated coding evidence on every build, with evidence labels showing which positions are Supported versus Estimated. This page answers the follow-up question: which model delivers the most coding capability per dollar spent.
Is Claude or GPT better for coding?
The live coding leaderboard reflects the latest results. Evidence labels matter as much as rank: a Supported position slightly below an Estimated one is often the safer pick for production coding work.
What is the best open-source LLM for coding?
Use the open-weight rows on the coding leaderboard for capability order, and the open-source rankings for the full downloadable-weights picture. Open coding leaders trail the proprietary top tier on raw score but often win decisively on this page’s capability-per-dollar measure.
Which coding model gives the best value?
That is what this ranking measures: weighted coding score divided by output token price. A cheap model with a low coding score can rank high here, so read each row's raw coding score next to its price before you shortlist it.
Explore More
Know when it’s worth switching models
The model to choose, the cheaper alternative, and the release we would wait on.
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