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Model comparison

GPT-5.4 nano vs Kimi K2.5 (Reasoning)

Updated July 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

GPT-5.4 nano

OpenAI

66.0/100

Supported · Public rank #28

90% interval 55.5–76.4

Kimi K2.5 (Reasoning)

Moonshot AI

58.5/100

Estimated · Public rank #64

90% interval 47.0–70.0

GPT-5.4 nano has the higher public score estimate, 65.98 versus 58.53, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

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.

  • Long documents

    Prompts that approach the documented context limit

    GPT-5.4 nano

    GPT-5.4 nano has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    GPT-5.4 nano

    GPT-5.4 nano 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

    GPT-5.4 nano

    GPT-5.4 nano 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

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    GPT-5.4 nano

    GPT-5.4 nano 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

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    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-5.4 nano only
9
Kimi K2.5 (Reasoning) only
5
Like-for-like categories
1 / 8

2 categories use 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.

Multimodal

Like-for-like
GPT-5.4 nano
66.1
Kimi K2.5 (Reasoning)
78.5
Weighted basis
1 vs 1 rows
Reading
Kimi K2.5 (Reasoning) leads

Agentic

Directional only
GPT-5.4 nano
42.9
Kimi K2.5 (Reasoning)
55.0
Weighted basis
2 vs 2 rows
Reading
Directional only

Knowledge

Directional only
GPT-5.4 nano
43.8
Kimi K2.5 (Reasoning)
87.2
Weighted basis
2 vs 2 rows
Reading
Directional only

Coding

Not comparable
GPT-5.4 nano
Not measured
Kimi K2.5 (Reasoning)
76.8
Weighted basis
0 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-5.4 nano
Not measured
Kimi K2.5 (Reasoning)
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
GPT-5.4 nano
21.0
Kimi K2.5 (Reasoning)
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.4 nano
Not measured
Kimi K2.5 (Reasoning)
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.4 nano
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-5.4 nano
$0.00082
Fits in one request
Kimi K2.5 (Reasoning)
$0.0021
Fits in one request

GPT-5.4 nano has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GPT-5.4 nano
$0.01375
Fits in one request
Kimi K2.5 (Reasoning)
$0.039
Fits in one request

GPT-5.4 nano 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-5.4 nano
$0.0205
Fits in one request
Kimi K2.5 (Reasoning)
$0.162
Fits in one request
Cached input priced at the published list-input rate

GPT-5.4 nano 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.

Cached-input rate

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

GPT-5.4 nano

$0.02 per 1M cached input tokens

OpenAI pricing

Kimi K2.5 (Reasoning)

Not published

Provider availability

GPT-5.4 nano

Generally Available · OpenAI Responses API

OpenAI model catalog

Kimi K2.5 (Reasoning)

Not sourced

Reasoning profile

GPT-5.4 nano

Reasoning

Kimi K2.5 (Reasoning)

Reasoning

Weight access

GPT-5.4 nano

Proprietary

Kimi K2.5 (Reasoning)

Proprietary

License

GPT-5.4 nano

Proprietary

Kimi K2.5 (Reasoning)

Proprietary

Release date

GPT-5.4 nano

2026-03-17

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-5.4 nano has the higher public score estimate, 65.98 versus 58.53, but the 90% score intervals overlap.
Workload cost
Repository review: $0.01375 vs $0.039. Cache-heavy agent loop: $0.0205 vs $0.162.
Context tradeoff
GPT-5.4 nano has the larger documented window (400K).

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 evidence18 rows

Agentic

  • Terminal-Bench 2.0

    GPT-5.4 nano46.3%
    Source
    Kimi K2.5 (Reasoning)50.8%
    Source

    Kimi K2.5 (Reasoning) leads this result

  • OSWorld-Verified

    GPT-5.4 nano39%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • MCP Atlas

    GPT-5.4 nano56.1%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • Toolathlon

    GPT-5.4 nano35.5%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • τ²-bench results

    GPT-5.4 nano92.5%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • BrowseComp

    GPT-5.4 nano—
    Kimi K2.5 (Reasoning)60.6%
    Source

    Not directly comparable

  • Gert Labs

    GPT-5.4 nano—
    Kimi K2.5 (Reasoning)32.58%
    Source

    Not directly comparable

Coding

  • Vibe Code Bench

    Shared source
    GPT-5.4 nano26.10%
    Kimi K2.5 (Reasoning)17.54%

    GPT-5.4 nano leads this result

  • SWE-bench Verified

    GPT-5.4 nano—
    Kimi K2.5 (Reasoning)76.8%
    Source

    Not directly comparable

Knowledge

  • GPQA

    GPT-5.4 nano82.8%
    Source
    Kimi K2.5 (Reasoning)87.6%
    Source

    Kimi K2.5 (Reasoning) leads this result

  • HLE

    GPT-5.4 nano37.7%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • HLE w/o tools

    GPT-5.4 nano24.3%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • MMLU-Pro

    GPT-5.4 nano—
    Kimi K2.5 (Reasoning)87.1%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    GPT-5.4 nano25.860%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-5.4 nano6.250%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

  • AIME 2025

    GPT-5.4 nano—
    Kimi K2.5 (Reasoning)96.1%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-5.4 nano66.1%
    Source
    Kimi K2.5 (Reasoning)78.5%
    Source

    Kimi K2.5 (Reasoning) leads this result

  • MMMU-Pro w/ Python

    GPT-5.4 nano69.5%
    Source
    Kimi K2.5 (Reasoning)—

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.4 nano or Kimi K2.5 (Reasoning)?

GPT-5.4 nano has the higher public score estimate, 65.98 versus 58.53, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, GPT-5.4 nano 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-5.4 nano or Kimi K2.5 (Reasoning)?

The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which costs less, GPT-5.4 nano or Kimi K2.5 (Reasoning)?

For the stated presets, chat costs $0.00082 on GPT-5.4 nano and $0.0021 on Kimi K2.5 (Reasoning); repository review costs $0.01375 and $0.039; the cache-heavy agent loop costs $0.0205 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-5.4 nano or Kimi K2.5 (Reasoning)?

GPT-5.4 nano has the larger documented context window: 400K, compared with 256K.

Related comparisons

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Last updated July 30, 2026

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