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. | — | 48B |
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. | 200k | 1M |
| 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. | 46% | — |
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. | 33.4% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 59.4% | — |
| Overview | ||
| Company | Anthropic | Moonshot AI |
| Release date | Jun 20 2024 | Oct 30 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude 3.5 SonnetvsGPT-6.1 SolKimi LinearvsGPT-6.1 SolClaude 3.5 SonnetvsGemini 4 ArgonKimi LinearvsGemini 4 ArgonClaude 3.5 SonnetvsMuse Spark OpenKimi LinearvsMuse Spark OpenClaude 3.5 SonnetvsGrok 4.7Kimi LinearvsGrok 4.7Claude 3.5 SonnetvsDeepSeek-V4.1-Flash-1017Kimi LinearvsDeepSeek-V4.1-Flash-1017Claude 3.5 SonnetvsMistral Large 4Kimi LinearvsMistral Large 4Frequently asked questions
Claude 3.5 Sonnet and Kimi Linear don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Claude 3.5 Sonnet shipped 497 days before Kimi Linear, so benchmark comparisons should account for the intervening progress.
Context windows are 200k (Claude 3.5 Sonnet) vs 1M (Kimi Linear). Claude 3.5 Sonnet is closed, while Kimi Linear is open weight.
Direct benchmark comparisons are unavailable — Claude 3.5 Sonnet and Kimi Linear don't publish scores on any of the same benchmarks.