Long documents
Prompts that approach the documented context limit
Kimi K3.1 Pro
Kimi K3.1 Pro has the larger documented context window.
Confidence: documented
GPT-5.4 nano vs Kimi K3.1 Pro
Updated October 23, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Share or export
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 5
- GPT-5.4 nano only
- 18
- Kimi K3.1 Pro only
- 37
- Like-for-like categories
- 0 / 8
Supported: GPT-5.4 nanoHow the comparison works
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
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-ratesCache-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-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Kimi K3.1 Pro is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Kimi K3.1 Pro is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
29.6GPT-5.4 nano—Kimi K3.1 Pro
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
18 GPT-5.4 nano only
5 Shared
37 Kimi K3.1 Pro only
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
HLEKnowledge
Normalized gap 32.3GPT-5.4 nano:37.7%Kimi K3.1 Pro:70%HLE w/o toolsKnowledge
Normalized gap 26.8GPT-5.4 nano:24.3%Kimi K3.1 Pro:51.1%MMMU-ProMultimodal
Normalized gap 18.0GPT-5.4 nano:66.1%Kimi K3.1 Pro:84.1%GPQAKnowledge
Normalized gap 11.4GPT-5.4 nano:82.8%Kimi K3.1 Pro:94.2%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Multimodal
Directional only- GPT-5.4 nano
- 52.7
- #49/56
- Kimi K3.1 Pro
- 98.0
- #1/56
- Basis
- Provisional lane · 1 vs 3 weighted rows
- Reading
- Directional only
Agentic
Not comparable- GPT-5.4 nano
- 32.9
- Supported · #90/132
- Kimi K3.1 Pro
- Not ranked
- Basis
- BenchAlign v5.8 lane · 6 vs 16 public rows
- Reading
- Not comparable
Coding
Not comparable- GPT-5.4 nano
- 29.6
- Supported · #100/153
- Kimi K3.1 Pro
- Not ranked
- Basis
- BenchAlign v5.8 lane · 4 vs 6 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.4 nano
- 40.6
- Unranked · 4 rankable rows
- Kimi K3.1 Pro
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5.4 nano
- 39.3
- Supported · #122/187
- Kimi K3.1 Pro
- Not ranked
- Basis
- BenchAlign v5.8 lane · 5 vs 5 public rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.4 nano
- Not ranked
- Kimi K3.1 Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.4 nano
- 93.3
- #12/153
- Kimi K3.1 Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.4 nano
- 52.3
- Unranked · 2 rankable rows
- Kimi K3.1 Pro
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.4 nano | Kimi K3.1 Pro | Basis | Reading |
|---|---|---|---|---|
| Multimodal | 52.7#49/56 | 98.0#1/56 | Directional onlyProvisional lane · 1 vs 3 weighted rows | Directional only |
| Agentic | 32.9Supported · #90/132 | Not ranked | Not comparableBenchAlign v5.8 lane · 6 vs 16 public rows | Not comparable |
| Coding | 29.6Supported · #100/153 | Not ranked | Not comparableBenchAlign v5.8 lane · 4 vs 6 public rows | Not comparable |
| Reasoning | 40.6Unranked · 4 rankable rows | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Knowledge | 39.3Supported · #122/187 | Not ranked | Not comparableBenchAlign v5.8 lane · 5 vs 5 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 93.3#12/153 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 52.3Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
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 K3.1 Pro
- $0.007
- 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 K3.1 Pro
- $0.13
- 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 K3.1 Pro
- $0.18
- Fits in one request
GPT-5.4 nano has the lower modeled cost
Costs use the listed standard API rates.
Cached input falls back to the list input rate only where a cached rate is unpublished
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 K3.1 Pro
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.4 nano
Kimi K3.1 Pro
1.05M
API model ID
GPT-5.4 nano
gpt-5.4-nano
OpenAI GPT-5.4 nano model documentationKimi K3.1 Pro
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 pricingKimi K3.1 Pro
$0.2 per 1M cached input tokens
Documented inputs
GPT-5.4 nano
text, image
OpenAI model catalogKimi K3.1 Pro
Not sourced
Documented outputs
GPT-5.4 nano
Kimi K3.1 Pro
Not sourced
Provider availability
GPT-5.4 nano
Generally Available · OpenAI Responses API
OpenAI model catalogKimi K3.1 Pro
Not sourced
Reasoning profile
GPT-5.4 nano
Reasoning
Kimi K3.1 Pro
Reasoning
Weight access
GPT-5.4 nano
Proprietary
Kimi K3.1 Pro
Pending
License
GPT-5.4 nano
Proprietary
Kimi K3.1 Pro
Pending
Release date
GPT-5.4 nano
2026-03-17
Kimi K3.1 Pro
2026-10-23
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- Repository review: $0.01375 vs $0.13. Cache-heavy agent loop: $0.0205 vs $0.18.
- Context tradeoff
- Kimi K3.1 Pro has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-5.4 nano or Kimi K3.1 Pro?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-5.4 nano or Kimi K3.1 Pro?
Kimi K3.1 Pro is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT-5.4 nano or Kimi K3.1 Pro?
Kimi K3.1 Pro is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-5.4 nano or Kimi K3.1 Pro?
For the stated presets, chat costs $0.00082 on GPT-5.4 nano and $0.007 on Kimi K3.1 Pro; repository review costs $0.01375 and $0.13; the cache-heavy agent loop costs $0.0205 and $0.18. Costs use the listed standard API rates.
Which has the larger context window, GPT-5.4 nano or Kimi K3.1 Pro?
Kimi K3.1 Pro has the larger documented context window: 1.05M, compared with 400K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence60 rows
Agentic
Terminal-Bench 2.0
GPT-5.4 nano46.3%SourceKimi K3.1 Pro—Not directly comparable
OSWorld-Verified
GPT-5.4 nano39%SourceKimi K3.1 Pro—Not directly comparable
MCP Atlas
GPT-5.4 nano56.1%SourceKimi K3.1 Pro—Not directly comparable
Toolathlon
GPT-5.4 nano35.5%SourceKimi K3.1 Pro—Not directly comparable
τ²-bench results
GPT-5.4 nano92.5%SourceKimi K3.1 Pro—Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.4 nano41.6%SourceKimi K3.1 Pro—Not directly comparable
Terminal-Bench 4.0
GPT-5.4 nano—Kimi K3.1 Pro65.70%SourceNot directly comparable
Terminal-Bench-Science 0.1
GPT-5.4 nano—Kimi K3.1 Pro38.1%SourceNot directly comparable
HLE w/ tools
GPT-5.4 nano—Kimi K3.1 Pro70.0%SourceNot directly comparable
BrowseComp
GPT-5.4 nano—Kimi K3.1 Pro92.1%SourceNot directly comparable
BioMysteryBench
GPT-5.4 nano—Kimi K3.1 Pro80.6%SourceNot directly comparable
Agents' Last Exam
GPT-5.4 nano—Kimi K3.1 Pro43.2%SourceNot directly comparable
Agents' Last Exam (CLI)
GPT-5.4 nano—Kimi K3.1 Pro44.2%SourceNot directly comparable
APEX-Agents
GPT-5.4 nano—Kimi K3.1 Pro67.2%SourceNot directly comparable
Frontier Finance
GPT-5.4 nano—Kimi K3.1 Pro58.6%SourceNot directly comparable
HANDBOOK.md
GPT-5.4 nano—Kimi K3.1 Pro44.6%SourceNot directly comparable
OSWorld 2.0
GPT-5.4 nano—Kimi K3.1 Pro79.6%SourceNot directly comparable
Business Operation
GPT-5.4 nano—Kimi K3.1 Pro37.8%SourceNot directly comparable
Financial Modeling
GPT-5.4 nano—Kimi K3.1 Pro57.6%SourceNot directly comparable
Financial Research
GPT-5.4 nano—Kimi K3.1 Pro70.4%SourceNot directly comparable
Kimi Hallu Bench Hard (tools)
GPT-5.4 nano—Kimi K3.1 Pro8.3%SourceNot directly comparable
ExploitGym
GPT-5.4 nano—Kimi K3.1 Pro20.7%SourceNot directly comparable
Coding
Terminal-Bench 2.0
GPT-5.4 nano46.3%SourceKimi K3.1 Pro—Not directly comparable
Vibe Code Bench
GPT-5.4 nano26.10%SourceKimi K3.1 Pro—Not directly comparable
LiveCodeBench (Vals)
GPT-5.4 nano84.0%SourceKimi K3.1 Pro—Not directly comparable
SWE-bench (Vals)
GPT-5.4 nano69.8%SourceKimi K3.1 Pro—Not directly comparable
DeepSWE
GPT-5.4 nano—Kimi K3.1 Pro75.5%SourceNot directly comparable
ProgramBench (binary)
GPT-5.4 nano—Kimi K3.1 Pro31.5%SourceNot directly comparable
Kimi Code Bench v2
GPT-5.4 nano—Kimi K3.1 Pro77.1%SourceNot directly comparable
Kimi Code Bench 3.0 Long-horizon
GPT-5.4 nano—Kimi K3.1 Pro61.2%SourceNot directly comparable
sweMarathon
GPT-5.4 nano—Kimi K3.1 Pro51.7%SourceNot directly comparable
PostTrainBench v1.2
GPT-5.4 nano—Kimi K3.1 Pro36.5%SourceNot directly comparable
Reasoning
Multimodal
ExtractBench (LlamaIndex)
GPT-5.4 nano74.90%SourceKimi K3.1 Pro—Not directly comparable
ParseBench
GPT-5.4 nano43.35%SourceKimi K3.1 Pro—Not directly comparable
MMMU-Pro
GPT-5.4 nano66.1%SourceKimi K3.1 Pro84.1%SourceKimi K3.1 Pro leads this result
MMMU-Pro w/ Python
GPT-5.4 nano69.5%SourceKimi K3.1 Pro90%SourceKimi K3.1 Pro leads this result
OfficeQA Pro
GPT-5.4 nano—Kimi K3.1 Pro67.4%SourceNot directly comparable
OfficeQA Pro v2
GPT-5.4 nano—Kimi K3.1 Pro64.4%SourceNot directly comparable
BenchCAD Vision2Code (no tools)
GPT-5.4 nano—Kimi K3.1 Pro0.728SourceNot directly comparable
BenchCAD Vision2Code (tools)
GPT-5.4 nano—Kimi K3.1 Pro0.927SourceNot directly comparable
CharXiv
GPT-5.4 nano—Kimi K3.1 Pro93.4%SourceNot directly comparable
CharXiv w/o tools
GPT-5.4 nano—Kimi K3.1 Pro89%SourceNot directly comparable
Chartography (tools)
GPT-5.4 nano—Kimi K3.1 Pro90.2%SourceNot directly comparable
BabyVision w/ Python
GPT-5.4 nano—Kimi K3.1 Pro95.4%SourceNot directly comparable
ZeroBench
GPT-5.4 nano—Kimi K3.1 Pro39.0%SourceNot directly comparable
ZeroBench w/ Python
GPT-5.4 nano—Kimi K3.1 Pro63.0%SourceNot directly comparable
GDP.pdf (Gemini judge)
GPT-5.4 nano—Kimi K3.1 Pro34.6%SourceNot directly comparable
GDP.pdf w/ Python (Gemini judge)
GPT-5.4 nano—Kimi K3.1 Pro85.7%SourceNot directly comparable
PerceptionBench
GPT-5.4 nano—Kimi K3.1 Pro68.3%SourceNot directly comparable
Knowledge
GPQA
GPT-5.4 nano82.8%SourceKimi K3.1 Pro94.2%SourceKimi K3.1 Pro leads this result
HLE
GPT-5.4 nano37.7%SourceKimi K3.1 Pro70%SourceKimi K3.1 Pro leads this result
HLE w/o tools
GPT-5.4 nano24.3%SourceKimi K3.1 Pro51.1%SourceKimi K3.1 Pro leads this result
GPQA Diamond (Vals)
GPT-5.4 nano77.5%SourceKimi K3.1 Pro—Not directly comparable
MMLU-Pro (Vals)
GPT-5.4 nano77.2%SourceKimi K3.1 Pro—Not directly comparable
GPQA-D
GPT-5.4 nano—Kimi K3.1 Pro94.2%SourceNot directly comparable
Kimi Hallu Bench
GPT-5.4 nano—Kimi K3.1 Pro5.4%SourceNot directly comparable
Math
60 public results · 5 shared
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Last updated October 23, 2026