Model A

OpenAI

66.6/100

Supported · Public rank #30

90% interval 56.1–77.1

GPT-5.4 nano vs Kimi K3

Updated August 19, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Kimi K3

Moonshot AI

80.5/100

Supported · Public rank #5

90% interval 77.7–83.4

Decision reading

Kimi K3 has the higher public score, 80.53 versus 66.62, and the 90% score intervals do not overlap.

7 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
Kimi K3
Kimi K3 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. Costs use the listed standard API rates.
Confidence: listed-rates

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.

6 GPT-5.4 nano only

7 Shared

33 Kimi K3 only

Shared results: 7
GPT-5.4 nano only: 6
Kimi K3 only: 33
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.

Knowledge

Like-for-like

GPT-5.4 nano: 43.8
Kimi K3: 61.0
Weighted basis: 2 vs 2 rows
Reading: Kimi K3 leads

Agentic

Directional only

GPT-5.4 nano: 42.9
Kimi K3: 89.5
Weighted basis: 2 vs 2 rows
Reading: Directional only

Multimodal

Directional only

GPT-5.4 nano: 66.1
Kimi K3: 78.5
Weighted basis: 1 vs 3 rows
Reading: Directional only

Coding

Not comparable

GPT-5.4 nano: Not measured
Kimi K3: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Reasoning

Not comparable

GPT-5.4 nano: Not measured
Kimi K3: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Math

Not comparable

GPT-5.4 nano: 21.0
Kimi K3: Not measured
Weighted basis: 2 vs 0 rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.4 nano: Not measured
Kimi K3: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Instruction following

Not comparable

GPT-5.4 nano: Not measured
Kimi K3: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for GPT-5.4 nano and Kimi K3
Category	GPT-5.4 nano	Kimi K3	Weighted basis	Reading
Knowledge	43.8	61.0	Like-for-like2 vs 2 rows	Kimi K3 leads
Agentic	42.9	89.5	Directional only2 vs 2 rows	Directional only
Multimodal	66.1	78.5	Directional only1 vs 3 rows	Directional only
Coding	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Reasoning	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Math	21.0	Not measured	Not comparable2 vs 0 rows	Not comparable
Multilingual	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Instruction following	Not measured	Not measured	Not comparable0 vs 0 rows	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.

Terminal-Bench 2.0
Agentic
GPT-5.4 nano: 46.3%Kimi K3: 88.3%Normalized gap 42.0GPT-5.4 nano source Kimi K3 source
HLE
Knowledge
GPT-5.4 nano: 37.7%Kimi K3: 56%Normalized gap 18.3GPT-5.4 nano source Kimi K3 source
MMMU-Pro
Multimodal
GPT-5.4 nano: 66.1%Kimi K3: 81.6%Normalized gap 15.5GPT-5.4 nano source Kimi K3 source
GPQA
Knowledge
GPT-5.4 nano: 82.8%Kimi K3: 93.5%Normalized gap 10.7GPT-5.4 nano source Kimi K3 source

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 K3: $0.0105; 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 K3: $0.195; 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 K3: $0.27; Fits in one request

GPT-5.4 nano has the lower modeled cost

Costs use the listed standard API rates.

Specification differences

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

SpecificationGPT-5.4 nanoKimi K3

Context window

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

GPT-5.4 nano

400K

OpenAI GPT-5.4 nano model documentation

Kimi K3

1.05M

API model ID

GPT-5.4 nano

gpt-5.4-nano

OpenAI GPT-5.4 nano model documentation

Kimi K3

Not sourced

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 K3

$0.3 per 1M cached input tokens

Documented inputs

GPT-5.4 nano

text, image

OpenAI model catalog

Kimi K3

Not sourced

Documented outputs

GPT-5.4 nano

text

OpenAI model catalog

Kimi K3

Not sourced

Provider availability

GPT-5.4 nano

Generally Available · OpenAI Responses API

OpenAI model catalog

Kimi K3

Not sourced

Reasoning profile

GPT-5.4 nano

Reasoning

Kimi K3

Reasoning

Weight access

GPT-5.4 nano

Proprietary

Kimi K3

Pending

License

GPT-5.4 nano

Proprietary

Kimi K3

Pending

Release date

GPT-5.4 nano

2026-03-17

Kimi K3

2026-07-16

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: Kimi K3 has the higher public score, 80.53 versus 66.62, and the 90% score intervals do not overlap.
Workload cost: Repository review: $0.01375 vs $0.195. Cache-heavy agent loop: $0.0205 vs $0.27.
Context tradeoff: Kimi K3 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 evidence46 rows

Agentic

Terminal-Bench 2.0
GPT-5.4 nano46.3%
Source
Kimi K388.3%
Source
Kimi K3 leads this result
OSWorld-Verified
GPT-5.4 nano39%
Source
Kimi K3—
Not directly comparable
MCP Atlas
GPT-5.4 nano56.1%
Source
Kimi K384.2%
Source
Kimi K3 leads this result
Toolathlon
GPT-5.4 nano35.5%
Source
Kimi K3—
Not directly comparable
τ²-bench results
GPT-5.4 nano92.5%
Source
Kimi K3—
Not directly comparable
BrowseComp
GPT-5.4 nano—
Kimi K391.2%
Source
Not directly comparable
DeepSearchQA
GPT-5.4 nano—
Kimi K395.0%
Source
Not directly comparable
Toolathlon-Verified
GPT-5.4 nano—
Kimi K373.2%
Source
Not directly comparable
AutomationBench
GPT-5.4 nano—
Kimi K330.8%
Source
Not directly comparable
JobBench
GPT-5.4 nano—
Kimi K352.9%
Source
Not directly comparable
APEX-Agents
GPT-5.4 nano—
Kimi K337.6%
Source
Not directly comparable
SpreadsheetBench 2
GPT-5.4 nano—
Kimi K334.8%
Source
Not directly comparable
DECK-Bench
GPT-5.4 nano—
Kimi K373.5%
Source
Not directly comparable

Coding

Vibe Code Bench
GPT-5.4 nano26.10%
Source
Kimi K3—
Not directly comparable
deepSwe
GPT-5.4 nano—
Kimi K367.5%
Source
Not directly comparable
cursorBench32
GPT-5.4 nano—
Kimi K360.8%
Source
Not directly comparable
FrontierSWE
GPT-5.4 nano—
Kimi K381.2%
Source
Not directly comparable
ProgramBench
GPT-5.4 nano—
Kimi K377.8%
Source
Not directly comparable
Kimi Code Bench v2
GPT-5.4 nano—
Kimi K372.9%
Source
Not directly comparable
sweMarathon
GPT-5.4 nano—
Kimi K342%
Source
Not directly comparable
PostTrain Bench
GPT-5.4 nano—
Kimi K336.6%
Source
Not directly comparable
MLS-Bench Lite
GPT-5.4 nano—
Kimi K348.3%
Source
Not directly comparable
VulcanBench v3
GPT-5.4 nano—
Kimi K373.9%
Source
Not directly comparable
APEX-SWE
GPT-5.4 nano—
Kimi K348.0%
Source
Not directly comparable
EEBench
GPT-5.4 nano—
Kimi K338.3%
Source
Not directly comparable
InferenceEval
GPT-5.4 nano—
Kimi K339.8%
Source
Not directly comparable
KernelBench Internal
GPT-5.4 nano—
Kimi K368.5%
Source
Not directly comparable

Knowledge

GPQA
GPT-5.4 nano82.8%
Source
Kimi K393.5%
Source
Kimi K3 leads this result
HLE
GPT-5.4 nano37.7%
Source
Kimi K356%
Source
Kimi K3 leads this result
HLE w/o tools
GPT-5.4 nano24.3%
Source
Kimi K343.5%
Source
Kimi K3 leads this result
GPQA-D
GPT-5.4 nano—
Kimi K393.5%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
GPT-5.4 nano25.860%
Source
Kimi K3—
Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.4 nano6.250%
Source
Kimi K3—
Not directly comparable

Multimodal

MMMU-Pro
GPT-5.4 nano66.1%
Source
Kimi K381.6%
Source
Kimi K3 leads this result
MMMU-Pro w/ Python
GPT-5.4 nano69.5%
Source
Kimi K383.4%
Source
Kimi K3 leads this result
OfficeQA Pro
GPT-5.4 nano—
Kimi K363.3%
Source
Not directly comparable
CharXiv w/o tools
GPT-5.4 nano—
Kimi K384.8%
Source
Not directly comparable
CharXiv
GPT-5.4 nano—
Kimi K391.3%
Source
Not directly comparable
MathVision
GPT-5.4 nano—
Kimi K394.3%
Source
Not directly comparable
MathVision w/ Python
GPT-5.4 nano—
Kimi K397.8%
Source
Not directly comparable
BabyVision w/ Python
GPT-5.4 nano—
Kimi K385.7%
Source
Not directly comparable
ZeroBench
GPT-5.4 nano—
Kimi K323.0%
Source
Not directly comparable
ZeroBench w/ Python
GPT-5.4 nano—
Kimi K341.0%
Source
Not directly comparable
WorldVQA ForceAnswer
GPT-5.4 nano—
Kimi K351.0%
Source
Not directly comparable
OmniDocBench
GPT-5.4 nano—
Kimi K391.1%
Source
Not directly comparable
PerceptionBench
GPT-5.4 nano—
Kimi K358.5%
Source
Not directly comparable

Frequently asked questions

Which is better, GPT-5.4 nano or Kimi K3?

Kimi K3 has the higher public score, 80.53 versus 66.62, 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-5.4 nano or Kimi K3?

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 K3?

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 K3?

For the stated presets, chat costs $0.00082 on GPT-5.4 nano and $0.0105 on Kimi K3; repository review costs $0.01375 and $0.195; the cache-heavy agent loop costs $0.0205 and $0.27. Costs use the listed standard API rates.

Which has the larger context window, GPT-5.4 nano or Kimi K3?

Kimi K3 has the larger documented context window: 1.05M, compared with 400K.

Related comparisons

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Last updated August 19, 2026

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GPT-5.4 nano vs Kimi K3

Which one for your work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Coding work

Agentic work

What is actually comparable

Category results, on a stated basis

Knowledge

Agentic

Multimodal

Coding

Reasoning

Math

Multilingual

Instruction following

Shape of the matched evidence

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

Benchmark evidence

Agentic

Coding

Knowledge

Math

Multimodal

Frequently asked questions

Which is better, GPT-5.4 nano or Kimi K3?

Which is better for coding, GPT-5.4 nano or Kimi K3?

Which is better for agentic tasks, GPT-5.4 nano or Kimi K3?

Which costs less, GPT-5.4 nano or Kimi K3?

Which has the larger context window, GPT-5.4 nano or Kimi K3?

Related comparisons

Watch GPT-5.4 nano vs Kimi K3