Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | 1T | 0.6B/1.7B/4B/8B/14B/30B/32B/235B |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 256k | — |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $0.95 | — |
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. | $4.00 | — |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | $0.19 | — |
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $0.60CoreWeave | $0.08DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $3.00CoreWeave | $0.22NextBit |
| $270CoreWeave · int4 | $30.00DeepInfra · fp8 | |
| These models have no shared benchmark scores. | ||
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | — | 6% |
DeepSWE 1.1Agentic coding — Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | 31% | — |
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. | 74% | — |
| Overview | ||
| Company | Moonshot AI | Qwen |
| Release date | Jun 12 2026 | Apr 29 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
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Kimi K2.7 Code and Qwen3 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Kimi K2.7 Code has a verified first-party price for an available API: $0.95 per million input tokens and $4.00 per million output tokens. No currently available pay-as-you-go API rate is tracked for Qwen3. Qwen3 shipped 409 days before Kimi K2.7 Code, so benchmark comparisons should account for the intervening progress.
Kimi K2.7 Code has 1T parameters, while Qwen3 has 0.6B/1.7B/4B/8B/14B/30B/32B/235B.
Direct benchmark comparisons are unavailable — Kimi K2.7 Code and Qwen3 don't publish scores on any of the same benchmarks.