CorX Labs

Head to head

Kimi K2 Thinking vs Claude Opus 4.5

Kimi K2 Thinking from Moonshot AI against Claude Opus 4.5 from Anthropic — specification, price and every benchmark both makers have published, in one table.

Benchmarks

Claude Opus 4.5 leads

Claude Opus 4.5 wins 3 of the 3 benchmarks both models report, Kimi K2 Thinking wins 0, consistently. The average gap across shared tests is 4.5 points.

Price

Kimi K2 Thinking is cheaper

On a 3:1 input-to-output mix, Kimi K2 Thinking costs $1.07 per million tokens against $10.00 for Claude Opus 4.5 — about 9.3× less. Remember that a reasoning model bills its thinking as output, so cost per answer can diverge much further than cost per token.

What actually differs

  • Kimi K2 Thinking has open weights under Modified MIT, so it can run on your own hardware with no per-token cost and no dependency on an API staying available. The other is API-only.
  • Kimi K2 Thinking takes 262K tokens of context against 200K — 1.3× more room for long documents or a large codebase.
  • Only Claude Opus 4.5 reads images. If your input includes screenshots, charts or documents, that decides it.

Scorecard

Which is better at what

Maths, coding, reasoning and the rest — one line each, averaged over the benchmarks both models actually report.

Which model scores higher in each capability category, averaged over the benchmarks all of them report.
CategoryKimi K2 ThinkingClaude Opus 4.5Better at this
ReasoningGPQA Diamond84.5%87.0%Claude Opus 4.5+2.5
MathsAIME 202594.5%96.0%Claude Opus 4.5+1.5
CodingSWE-bench Verified71.3%80.9%Claude Opus 4.5+9.6
KnowledgeNeither model reports thisNot comparable
MultimodalOnly one model reports thisNot comparable
Instruction followingNeither model reports thisNot comparable
Human preferenceNeither model reports thisNot comparable
Categories wonOut of 3 comparable03Claude Opus 4.5

Each category averages only the benchmarks every model here reports, so no one is credited for a test the other did not run. A category with no shared test is marked Not comparable rather than guessed at.

Side by side

Kimi K2 Thinking and Claude Opus 4.5, row by row

AttributeKimi K2 ThinkingMoonshot AIClaude Opus 4.5Anthropic
Specification
MakerWho built itMoonshot AIAnthropic
Released2025-112025-11
ParametersTotal, and active per token for a mixture of experts1T total / 32B activeNot reported
ArchitectureMoENot reported
Context windowHow much can go in at once262,144 tokens200,000 tokens
Max output131,072 tokens64,000 tokens
InputTextText, Image
ReasoningSpends extra tokens thinking before it answersYesYes
Tool callingYesYes
Knowledge cutoffNot reported2025-03
LicenceModified MITProprietary
Open weightsCan you download and run it yourselfYesNo
Price
Input priceUSD per million tokens in$0.60$5.00
Output priceUSD per million tokens out$2.50$25.00
Cached inputNot reported$0.50
Blended 3:1A 3-in-to-1-out million-token mix — a fairer single number than input price alone$1.07Cheapest$10.00
Price noteOpen weights — this is a representative hosting rate, not a first-party price. Running it yourself costs only hardware.First-party API rate.
Published benchmarks
GPQA Diamond198 graduate-level physics, chemistry and biology questions written to be Google-proof. PhD holders in the matching field score about 65%.84.5%87%Best
AIME 2025The American Invitational Mathematics Examination — 15 problems, integer answers, no partial credit. A standard test of multi-step maths reasoning.94.5%96%Best
SWE-bench Verified500 human-validated GitHub issues from real Python repositories. The model must produce a patch that makes the project's own tests pass.71.3%80.9%Best
LiveCodeBenchCompetitive-programming problems collected after each model's training cutoff, so contamination cannot inflate the score.83.1%Not reported
MMMUCollege-level questions that require reading charts, diagrams, tables and photographs alongside the text.Not reported82%
LinksHugging Face · Full pageFull page
Row verified2026-082026-08

Where these numbers come from

Every score on this page is a published figure, taken from the model's own card, system card, technical report or release post, or from a public leaderboard. CorX Labs did not run these evaluations. Most are self-reported by the lab that built the model, which means they were produced under that lab's own choice of prompt, scaffold and number of attempts — so treat them as a starting point for a shortlist, not as a settled ranking.

A score someone other than the model's maker measured is marked Independent and names its measurer. Those are the stronger numbers on this page — an outside harness has no reason to flatter anyone — and there are not many of them.

Where a figure has not been published, the cell reads Not reported rather than an estimate. Nothing here is inferred, interpolated or guessed. Each model records the month its row was last checked. Full method and caveats.

Questions

Kimi K2 Thinking or Claude Opus 4.5?

Is Kimi K2 Thinking better than Claude Opus 4.5?

Claude Opus 4.5 wins 3 of the 3 benchmarks both models report, Kimi K2 Thinking wins 0, consistently. The average gap across shared tests is 4.5 points.

Which is cheaper, Kimi K2 Thinking or Claude Opus 4.5?

On a 3:1 input-to-output mix, Kimi K2 Thinking costs $1.07 per million tokens against $10.00 for Claude Opus 4.5 — about 9.3× less. Remember that a reasoning model bills its thinking as output, so cost per answer can diverge much further than cost per token.

What is the difference between Kimi K2 Thinking and Claude Opus 4.5?

Kimi K2 Thinking has open weights under Modified MIT, so it can run on your own hardware with no per-token cost and no dependency on an API staying available. The other is API-only. Kimi K2 Thinking takes 262K tokens of context against 200K — 1.3× more room for long documents or a large codebase. Only Claude Opus 4.5 reads images. If your input includes screenshots, charts or documents, that decides it.