TokelyTHE SHORT ANSWER
The best coding model is the one that completes your repository tasks at an acceptable total cost and review burden. Token price alone cannot identify it. Compare a small shortlist on the same tasks, tool access and acceptance criteria, then measure cost per accepted change.
Pricing examples checked October 15, 2026. No coding benchmark was run for this article, and the shortlist below is not a measured ranking.
Which models belong in an initial shortlist?
For a compact comparison, consider Claude Sonnet 5.5, Claude Opus 5.5 and DeepSeek V4 Pro where their endpoint and tool support fit your coding client. They occupy different published price points; that makes them useful candidates to evaluate, not predetermined winners.
Tokely lists Sonnet 5.5 at $1.50 input/$7.50 output, Opus 5.5 at $3/$15 and DeepSeek V4 Pro at $0.88/$2.64 per million tokens. Tokely pricing.
At 1 million uncached input and 250,000 output tokens, the respective token subtotals are $3.375, $6.75 and $1.54. This assumes the published normal route and excludes caching, additional charges and any route adjustment. Equal token volumes do not imply equal work completed.
What should a coding evaluation include?
Choose real task categories: fixing a regression, adding a small feature, updating a dependency, writing a test and navigating unfamiliar code. Pin the repository commit and preserve the task instructions.
Give each candidate equivalent tools, time limits and access to project documentation. Record model settings and any context compression. If one model receives a richer environment, you are comparing workflows as well as models.
How do you define an accepted change?
Require relevant tests to pass, the requested behavior to be implemented and unrelated changes to be absent. Review the diff for maintainability and unnecessary dependencies. A patch that passes tests while changing the wrong behavior is not an accepted result.
Keep reviewer time as a separate metric. If a model produces a correct-looking patch that takes an hour to untangle, that effort belongs in the decision.
Why can an expensive model be cheaper per task?
Suppose a hypothetical lower-priced model costs $0.20 per attempt and succeeds on 50% of tasks in a fixed evaluation. Its aggregate cost per accepted task is $0.40. A model costing $0.30 per attempt with 90% acceptance costs about $0.333 per accepted task.
These are illustrative ratios, not observed model scores. They show why purchase decisions need outcome data.
How does caching affect coding sessions?
Project instructions, tool definitions and conversation history can recur across calls. Measure cache writes and reads rather than pricing all context as fresh input. Also track repair attempts: a long session can erase a low per-token price.
Use Tokely’s model catalog to verify the selected endpoint. For Claude Code, follow the Messages integration guide; an OpenAI-compatible chat endpoint alone is not sufficient. Choose a default only after the model meets your repository’s acceptance criteria.