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Model A
Claude Mythos Preview

Anthropic

Evidence status unavailable

90% interval unavailable

Claude Mythos Preview vs Kimi K2.7 Code

Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Kimi K2.7 Code

Moonshot AI

53.9/100

Estimated · Public rank #98

90% interval 42.4–65.5

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Chat turn cost

    1K fresh input + 500 output tokens

    Kimi K2.7 Code

    Kimi K2.7 Code has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Kimi K2.7 Code

    Kimi K2.7 Code has the lower estimated token cost for this stated workload. Claude Mythos Preview has no published cached-input rate, so cached tokens use its listed input rate. Kimi K2.7 Code has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

Show secondary and unsupported calls
  • Repository review cost

    50K fresh input + 3K output tokens

    Kimi K2.7 Code

    Kimi K2.7 Code has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    Not enough matched evidence

    A complete context comparison is not sourced.

    Confidence: limited

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
0
Claude Mythos Preview only
2
Kimi K2.7 Code only
8
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Not comparable
Claude Mythos Preview
Not measured
Kimi K2.7 Code
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Claude Mythos Preview
Not measured
Kimi K2.7 Code
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Claude Mythos Preview
Not measured
Kimi K2.7 Code
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Claude Mythos Preview
Not measured
Kimi K2.7 Code
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Claude Mythos Preview
Not measured
Kimi K2.7 Code
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Claude Mythos Preview
Not measured
Kimi K2.7 Code
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Claude Mythos Preview
Not measured
Kimi K2.7 Code
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Claude Mythos Preview
Not measured
Kimi K2.7 Code
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

Claude Mythos Preview
$0.0875
Fit state unavailable
Kimi K2.7 Code
$0.00295
Fits in one request

Kimi K2.7 Code has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Mythos Preview
$1.63
Fit state unavailable
Kimi K2.7 Code
$0.0595
Fits in one request

Kimi K2.7 Code has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Claude Mythos Preview
$6.75
Fit state unavailable
Cached input priced at the published list-input rate
Kimi K2.7 Code
$0.249
Fits in one request
Cached input priced at the published list-input rate

Kimi K2.7 Code has the lower modeled cost

Claude Mythos Preview has no published cached-input rate, so cached tokens use its listed input rate. Kimi K2.7 Code has no published cached-input rate, so cached tokens use its listed input rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Context window

Maximum documented context; output-token limits may be lower.

Claude Mythos Preview

Not sourced

Kimi K2.7 Code

256K

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Claude Mythos Preview

Kimi K2.7 Code

Not published

Documented inputs

Claude Mythos Preview

Not sourced

Kimi K2.7 Code

Not sourced

Documented outputs

Claude Mythos Preview

Not sourced

Kimi K2.7 Code

Not sourced

Provider availability

Claude Mythos Preview

Not sourced

Kimi K2.7 Code

Not sourced

Reasoning profile

Claude Mythos Preview

Reasoning

Kimi K2.7 Code

Reasoning

Weight access

Claude Mythos Preview

Proprietary

Kimi K2.7 Code

Open Weight

License

Claude Mythos Preview

Proprietary

Kimi K2.7 Code

Open Weight

Release date

Claude Mythos Preview

2026-04-07

Kimi K2.7 Code

2026-06-12

If you already use one of these models
Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
Repository review: $1.63 vs $0.0595. Cache-heavy agent loop: $6.75 vs $0.249.
Context tradeoff
A complete documented context comparison is not available.

Run the same representative tasks against both endpoints before changing production traffic.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Claude Mythos Preview
API / mo$112,500
Self-host / moNot listed
Break-even
Proprietary model — self-hosting not applicable.
Kimi K2.7 Code
API / mo$3,713
Self-host / mo$18,221
Break-even326M/day
Model the full break-even

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence10 rows

Agentic

  • CyberGym

    Claude Mythos Preview83.1%
    Source
    Kimi K2.7 Code

    Not directly comparable

  • ExploitGym

    Claude Mythos Preview17.5%
    Source
    Kimi K2.7 Code

    Not directly comparable

  • Kimi Claw 24/7

    Claude Mythos Preview
    Kimi K2.7 Code46.9%
    Source

    Not directly comparable

  • MCP Atlas

    Claude Mythos Preview
    Kimi K2.7 Code76%
    Source

    Not directly comparable

  • MCP Mark Verified

    Claude Mythos Preview
    Kimi K2.7 Code81.1%
    Source

    Not directly comparable

Coding

  • Kimi Code Bench v2

    Claude Mythos Preview
    Kimi K2.7 Code62.0%
    Source

    Not directly comparable

  • ProgramBench

    Claude Mythos Preview
    Kimi K2.7 Code53.6%
    Source

    Not directly comparable

  • MLS-Bench Lite

    Claude Mythos Preview
    Kimi K2.7 Code35.1%
    Source

    Not directly comparable

  • cursorBench32

    Claude Mythos Preview
    Kimi K2.7 Code49.7%
    Source

    Not directly comparable

  • EEBench

    Claude Mythos Preview
    Kimi K2.7 Code12.8%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude Mythos Preview or Kimi K2.7 Code?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Claude Mythos Preview or Kimi K2.7 Code?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, Claude Mythos Preview or Kimi K2.7 Code?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Claude Mythos Preview or Kimi K2.7 Code?

For the stated presets, chat costs $0.0875 on Claude Mythos Preview and $0.00295 on Kimi K2.7 Code; repository review costs $1.63 and $0.0595; the cache-heavy agent loop costs $6.75 and $0.249. Claude Mythos Preview has no published cached-input rate, so cached tokens use its listed input rate. Kimi K2.7 Code has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Mythos Preview or Kimi K2.7 Code?

A complete documented context-window comparison is not available.

Related comparisons

Last updated August 13, 2026

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