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Model comparison

GPT-5.6 Sol vs Kimi K2.5 (Reasoning)

Updated August 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

GPT-5.6 Sol

OpenAI

81.5/100

Supported · Public rank #4

90% interval 77.7–85.3

Kimi K2.5 (Reasoning)

Moonshot AI

58.6/100

Estimated · Public rank #66

90% interval 47.1–70.1

GPT-5.6 Sol has the higher public score, 81.48 versus 58.58, and the 90% score intervals do not overlap.

4 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.

  • Agentic work

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

    GPT-5.6 Sol

    GPT-5.6 Sol leads on the same 2 weighted benchmark rows.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    GPT-5.6 Sol

    GPT-5.6 Sol has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Kimi K2.5 (Reasoning)

    Kimi K2.5 (Reasoning) has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

Show secondary and unsupported calls

  • Cache-heavy agent loop cost

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

    Kimi K2.5 (Reasoning)

    Kimi K2.5 (Reasoning) has the lower estimated token cost for this stated workload. Kimi K2.5 (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

  • Repository review cost

    50K fresh input + 3K output tokens

    Kimi K2.5 (Reasoning)

    Kimi K2.5 (Reasoning) 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

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
4
GPT-5.6 Sol only
20
Kimi K2.5 (Reasoning) only
5
Like-for-like categories
2 / 8

1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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

Like-for-like
GPT-5.6 Sol
92.0
Kimi K2.5 (Reasoning)
55.0
Weighted basis
2 vs 2 rows
Reading
GPT-5.6 Sol leads

Multimodal

Like-for-like
GPT-5.6 Sol
83.0
Kimi K2.5 (Reasoning)
78.5
Weighted basis
1 vs 1 rows
Reading
GPT-5.6 Sol leads

Knowledge

Directional only
GPT-5.6 Sol
94.6
Kimi K2.5 (Reasoning)
87.2
Weighted basis
1 vs 2 rows
Reading
Directional only

Coding

Not comparable
GPT-5.6 Sol
64.6
Kimi K2.5 (Reasoning)
76.8
Weighted basis
1 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-5.6 Sol
92.5
Kimi K2.5 (Reasoning)
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Math

Not comparable
GPT-5.6 Sol
87.5
Kimi K2.5 (Reasoning)
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.6 Sol
Not measured
Kimi K2.5 (Reasoning)
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.6 Sol
Not measured
Kimi K2.5 (Reasoning)
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.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

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

GPT-5.6 Sol
$0.02
Fits in one request
Kimi K2.5 (Reasoning)
$0.0021
Fits in one request

Kimi K2.5 (Reasoning) has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GPT-5.6 Sol
$0.34
Fits in one request
Kimi K2.5 (Reasoning)
$0.039
Fits in one request

Kimi K2.5 (Reasoning) has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

GPT-5.6 Sol
$0.5
Fits in one request
Kimi K2.5 (Reasoning)
$0.162
Fits in one request
Cached input priced at the published list-input rate

Kimi K2.5 (Reasoning) has the lower modeled cost

Kimi K2.5 (Reasoning) 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.

GPT-5.6 Sol

1.05M

OpenAI model catalog

Kimi K2.5 (Reasoning)

256K

Cached-input rate

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

GPT-5.6 Sol

$0.5 per 1M cached input tokens

OpenAI pricing

Kimi K2.5 (Reasoning)

Not published

Provider availability

GPT-5.6 Sol

Generally Available · OpenAI Responses API

OpenAI model catalog

Kimi K2.5 (Reasoning)

Not sourced

Reasoning profile

GPT-5.6 Sol

Reasoning

Kimi K2.5 (Reasoning)

Reasoning

Weight access

GPT-5.6 Sol

Proprietary

Kimi K2.5 (Reasoning)

Proprietary

License

GPT-5.6 Sol

Proprietary

Kimi K2.5 (Reasoning)

Proprietary

Release date

GPT-5.6 Sol

2026-07-09

Kimi K2.5 (Reasoning)

2026-02-01

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
GPT-5.6 Sol has the higher public score, 81.48 versus 58.58, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.34 vs $0.039. Cache-heavy agent loop: $0.5 vs $0.162.
Context tradeoff
GPT-5.6 Sol has the larger documented window (1.05M).

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

Benchmark evidence

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

Browse raw public benchmark evidence29 rows

Agentic

  • Terminal-Bench 2.0

    GPT-5.6 Sol91.9%
    Source
    Kimi K2.5 (Reasoning)50.8%
    Source

    GPT-5.6 Sol leads this result

  • BrowseComp

    GPT-5.6 Sol92.2%
    Source
    Kimi K2.5 (Reasoning)60.6%
    Source

    GPT-5.6 Sol leads this result

  • OSWorld 2.0

    GPT-5.6 Sol62.6%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • CyberGym

    GPT-5.6 Sol84.5%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • ExploitGym

    GPT-5.6 Sol33.7%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • Toolathlon

    GPT-5.6 Sol58%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • Gert Labs

    GPT-5.6 Sol—
    Kimi K2.5 (Reasoning)32.58%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    GPT-5.6 Sol64.6%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-5.6 Sol91.9%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • deepSwe

    GPT-5.6 Sol72.7%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • FrontierCode 1.1 Extended

    GPT-5.6 Sol60.6%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • cursorBench32

    GPT-5.6 Sol67.2%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • VulcanBench v3

    GPT-5.6 Sol87.0%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • SWE-bench Verified

    GPT-5.6 Sol—
    Kimi K2.5 (Reasoning)76.8%
    Source

    Not directly comparable

  • Vibe Code Bench

    GPT-5.6 Sol—
    Kimi K2.5 (Reasoning)17.54%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GPT-5.6 Sol92.5%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • ARC-AGI-3

    GPT-5.6 Sol7.8%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • GeneBench-Pro

    GPT-5.6 Sol28.7%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

Knowledge

  • GPQA

    GPT-5.6 Sol94.6%
    Source
    Kimi K2.5 (Reasoning)87.6%
    Source

    GPT-5.6 Sol leads this result

  • GPQA-D

    GPT-5.6 Sol94.6%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • HealthBench Professional

    GPT-5.6 Sol60.5%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • HealthBench Hard

    GPT-5.6 Sol33.1%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • MMLU-Pro

    GPT-5.6 Sol—
    Kimi K2.5 (Reasoning)87.1%
    Source

    Not directly comparable

Math

  • FrontierMath (legacy)

    GPT-5.6 Sol89%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    GPT-5.6 Sol89.000%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-5.6 Sol83.000%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • AIME 2025

    GPT-5.6 Sol—
    Kimi K2.5 (Reasoning)96.1%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-5.6 Sol83%
    Source
    Kimi K2.5 (Reasoning)78.5%
    Source

    GPT-5.6 Sol leads this result

  • MMMU-Pro w/ Python

    GPT-5.6 Sol84.6%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.6 Sol or Kimi K2.5 (Reasoning)?

GPT-5.6 Sol has the higher public score, 81.48 versus 58.58, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, GPT-5.6 Sol or Kimi K2.5 (Reasoning)?

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, GPT-5.6 Sol or Kimi K2.5 (Reasoning)?

GPT-5.6 Sol leads the like-for-like agentic tasks comparison across 2 shared weighted benchmark rows.

Which costs less, GPT-5.6 Sol or Kimi K2.5 (Reasoning)?

For the stated presets, chat costs $0.02 on GPT-5.6 Sol and $0.0021 on Kimi K2.5 (Reasoning); repository review costs $0.34 and $0.039; the cache-heavy agent loop costs $0.5 and $0.162. Kimi K2.5 (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GPT-5.6 Sol or Kimi K2.5 (Reasoning)?

GPT-5.6 Sol has the larger documented context window: 1.05M, compared with 256K.

Related comparisons

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Last updated August 10, 2026

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