The best AI model for coding is the one that completes your kind of change inside the tools and budget you use. Shortlist Sonnet 5 or Opus 5 for a Claude coding workflow, Astra for difficult OpenAI-based work, and Luna or Haiku for bounded tasks with clear tests. Compare Gemini when its model and input capabilities fit your workflow.
This comparison covers hosted API models for coding agents. It separates documented capabilities, current Kunavo prices, and a repeatable selection method. For a general family comparison, see Claude vs GPT vs Gemini.
Choose the task before the model
| Your task | First shortlist | What decides |
|---|---|---|
| A test, explanation, or small isolated fix | Luna or Haiku 4.5 | Correct output without repeated repair |
| Routine changes across several files | Sonnet 5 and Opus 5 | Accepted edits, response time, and total charge |
| Hard debugging or a long autonomous change | Opus 5 or Astra; consider Fable 5.1 if needed | Progress through the difficult steps and passing checks |
| Code plus screenshots or large reference material | A suitable Claude or Gemini route | Actual image support, useful context, and verified edits |
Anthropic's model overview positions Opus 5 for complex agentic coding and Sonnet 5 for speed and intelligence. It recommends Fable 5.1 for demanding work when Opus evaluations fall short. OpenAI positions Astra for difficult end-to-end work, including coding. These vendor descriptions support candidates, not a cross-vendor winner.
Google lists Gemini 3.8 Flash as its current stable Flash for software engineering and agent execution. Gemini 3.1 Pro remains a preview option for complex reasoning. Choose a specific version; a family name alone leaves capabilities and release status ambiguous.
Compare current prices and context limits
USD per million standard uncached input/output tokens, from Kunavo's current catalog. Context limits describe capacity, not coding quality. Rows are grouped by use, not ranked by performance.
| Model | Candidate use | Context tokens | Input / output |
|---|---|---|---|
| GPT-5.6 Luna | Small, testable changes on a limited budget | 1,050,000 | $0.07 / $0.42 |
| Claude Haiku 4.5 | Bounded subtasks in a Claude workflow | 200,000 | $0.40 / $2.00 |
| Claude Sonnet 5 | Everyday coding and tool use | 1,000,000 | $2.00 / $10.00 |
| Claude Opus 5 | Complex changes and longer agent tasks | 1,000,000 | $2.00 / $10.00 |
| GPT-6 Astra | Difficult end-to-end work in an OpenAI workflow | 1,050,000 | $4.00 / $20.00 |
| Claude Fable 5.1 | Demanding work that still defeats the initial candidate | 1,000,000 | $7.00 / $35.00 |
| Gemini 3.8 Flash | Google's current Flash option for agentic coding | 1,048,576 | $0.525 / $2.625 |
| Gemini 3.1 Pro | Complex reasoning and multimodal analysis | 1,048,576 | $0.70 / $4.20 |
On this catalog, Sonnet 5 and Opus 5 have the same input and output rates. Compare their behavior and actual usage rather than assuming the Sonnet name makes it cheaper.
A catalog estimate is not the final bill's ceiling. Kunavo charges the higher of catalog cost and upstream cost multiplied by the model's markup factor. Cache reads, writes, reasoning output, and applicable long-context tiers also affect the calculation. See billing details and the current price list.
Check the agent's protocol and tools
For a custom provider, Codex needs the Responses protocol; a Chat Completions-only endpoint is insufficient. Claude Code's gateway documentation describes supported API formats and explicitly excludes official support for routing to non-Claude models. A provider menu is not evidence that every feature works.
Model-vendor features can also differ from a gateway route. OpenAI documents image input for Astra, but Kunavo's current GPT route does not expose vision. For screenshot-based work, select a documented image-capable route and confirm that your client actually sends the image. Check streaming, tool calls, reasoning settings, and output limits before moving a working task.
Use the relevant Codex, Claude Code, or OpenCode configuration. An API balance and a client's subscription are separate purchases.
Price the accepted change, including context
Divide the total charge for all attempts by the number of accepted tasks. Keep human corrections and elapsed time alongside that number. A low token rate can lose its advantage through retries; a higher rate can be worthwhile when it avoids them. Neither outcome follows from this price table alone.
Count fresh input, cache writes, cache reads, and output separately. Kunavo's Astra and Luna requests over 272,000 input tokens use twice the input/cache rate and 1.5 times output for the whole request. Gemini 3.1 Pro's threshold is 200,000. The listed 1M Claude models have no long-context surcharge, but sending more tokens still costs more.
Use benchmarks to shortlist, then test your repository
Read the model version, agent, tools, effort, task set, and number of runs beside a benchmark score. Anthropic's cost and intelligence study, for example, uses a 482-task SWE-bench Pro subset and explicitly says its scores are not comparable with the public leaderboard. Its costs are not Kunavo measurements.
- Choose a reproducible bug, a test addition, a multi-file change, a representative UI task, and one historically difficult task.
- Start each candidate from the same repository revision. Hold the prompt, tools, permissions, and budget steady; record model and effort settings.
- Define acceptance first: relevant tests, reviewed diff, and the requested behavior. Record failures as well as successful work.
- Compare total charges, completion time, and manual corrections. Repeat close results before changing the daily default.
Start with two candidates and one familiar agent. Then choose the payment route using the Codex, Cline, Kilo, or OpenCode API comparison. This makes a model decision actionable without changing every part of the workflow at once.
FAQ
What is the best AI model for coding?
Start with a model suited to the task and your agent. Sonnet 5 and Opus 5 are concrete Claude candidates; Astra is an OpenAI candidate for difficult work; Luna and Haiku are candidates for bounded tasks. Compare two on the same repository before choosing a daily default. This guide gives a sourced shortlist, not a universal quality ranking.
Which coding model should I try on a small budget?
GPT-5.6 Luna is a low-rate candidate in this table at $0.07 input and $0.42 output per million tokens on Kunavo. Try it on a small change with a clear test. Count failed attempts and corrections when deciding whether it is cheaper for the completed task.
Does a larger context window make a model better at coding?
It lets a request contain more material within the supported limit. It does not establish better edits, reliable retrieval of every detail, or fewer mistakes. Long requests may also enter a higher price tier. Compare the relevant files and completed result rather than ranking models by context size alone.
Is a coding model the same as a coding agent?
No. The model generates responses and tool calls; the agent supplies the editor or terminal workflow, tools, context management, and permissions. The same model can behave differently under different agents, prompts, tool access, and reasoning settings.
Official sources checked September 17, 2026. Recommendations use documented model positioning and catalog facts; no Kunavo comparative coding benchmark is claimed.