Claude Mythos 5vsKimi K2.6
Claude Mythos 5 | Kimi K2.6 | |
|---|---|---|
| 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. | — | 1T |
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. | 1M | 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. | — | $0.16 |
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. | — | $0.58Chutes |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | — | $2.52StreamLake |
| Benchmarks | ||
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. | 95.5% | 80.2% |
| BenchmarksPublished by one model only | ||
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. | — | 65% |
CursorBench v3.1Agentic coding — Cursor's own test of harder, real-world coding tasks inside a code editor. Higher is better. | — | 47.6% |
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. | — | 67% |
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | — | 89.6% |
Terminal-Bench 2.1Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | 88% | — |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | 88% | — |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | 64.5% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 90.5% |
OSWorld-VerifiedAgentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better. | 85% | — |
Arena Elo (Text)Community preference — Real people chat with two anonymous AIs side by side and vote for the answer they prefer. Votes become a chess-style Elo rating on arena.ai — it measures which AI people actually like, not test scores. Higher is better. | — | 1461 |
Arena Elo (Code)Community preference (code) — Like the text arena, but people vote on which AI writes better code. The votes become a chess-style Elo rating on arena.ai. Higher is better. | — | 1513 |
| Overview | ||
| Company | Anthropic | Moonshot AI |
| Release date | Jun 9 2026 | Apr 21 2026 |
| Access | Proprietary | Open Weight |
Other comparisons
Frequently asked questions
Claude Mythos 5 leads Kimi K2.6 on 1 of the 1 benchmark they both report (SWE-Bench Verified). Only Kimi K2.6 has a verified first-party API price: $0.95 per million input tokens and $4.00 per million output tokens. No pay-as-you-go API rate is tracked for Claude Mythos 5. Kimi K2.6 shipped 49 days before Claude Mythos 5, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Claude Mythos 5) vs 256k (Kimi K2.6). Claude Mythos 5 is proprietary, while Kimi K2.6 is open weight.
On SWE-Bench Verified, Claude Mythos 5 leads at 95.5% vs Kimi K2.6 at 80.2%.
Claude Mythos 5 was released by Anthropic on Jun 9 2026.
Kimi K2.6 was released by Moonshot AI on Apr 21 2026.
Claude Mythos 5 leads on SWE-Bench Verified — Claude Mythos 5 95.5% vs Kimi K2.6 80.2%.
Only Kimi K2.6 has a verified first-party API price: $0.95 per million input tokens and $4.00 per million output tokens. No pay-as-you-go API rate is tracked for Claude Mythos 5. Rates are pay-as-you-go API prices verified on August 18, 2026.
Claude Mythos 5 has a 1M context window; Kimi K2.6 has 256k.
Claude Mythos 5 is a proprietary model released by Anthropic. Kimi K2.6 is an open weight model released by Moonshot AI.