Compare AI models
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | 80B | — |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | 256k | — |
| API pricingUSD per 1M tokens · lower wins · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $0.30 | — |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | $1.50 | — |
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $0.12Parasail | — |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $0.80Parasail | — |
| Benchmarks | ||
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | 62.8% | 73.7% |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | 36.2% | 61.7% |
SWE-Bench ProAgentic coding — Can the AI fix real bugs in real software? It's handed actual problems from open-source projects and has to write code that genuinely solves them. Higher is better. | 44.3% | — |
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | 70.6% | — |
Next.js EvalsNext.js coding — Vercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | — | 58% |
| Overview | ||
| Company | Qwen | SpaceXAI |
| Release date | Feb 3 2026 | Mar 19 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
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Composer 2 leads Qwen3-Coder-Next on 2 of the 2 benchmarks they both report (SWE-Bench Multilingual, Terminal-Bench 2.0). Only Qwen3-Coder-Next has a verified first-party API price: $0.30 per million input tokens and $1.50 per million output tokens. No pay-as-you-go API rate is tracked for Composer 2. Qwen3-Coder-Next shipped 44 days before Composer 2, so benchmark comparisons should account for the intervening progress.
Qwen3-Coder-Next is open weight, while Composer 2 is closed.
On SWE-Bench Multilingual, Composer 2 leads at 73.7% vs Qwen3-Coder-Next at 62.8%. On Terminal-Bench 2.0, Composer 2 leads at 61.7% vs Qwen3-Coder-Next at 36.2%.