Coding
Like-for-like- GPT-5.5
- 67.6
- Supported · #7/151
- Kimi K2.7 Code
- 50.9
- Supported · #51/151
- Basis
- BenchAlign lane · 9 vs 5 public rows
- Reading
- GPT-5.5 leads · intervals overlap
GPT-5.5 vs Kimi K2.7 Code
Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.5 has the higher public score estimate, 72.07 versus 65.49, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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.
Coding work
Code generation, repair, and software-engineering tasks
GPT-5.5
GPT-5.5 leads on the public coding lane, 67.6 to 50.9, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Long documents
Prompts that approach the documented context limit
GPT-5.5
GPT-5.5 has the larger documented context window.
Confidence: documented
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
Show secondary and unsupported calls
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. Kimi K2.7 Code 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 K2.7 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
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Kimi K2.7 Code is scored on Estimated evidence for agentic, so the reading is 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.
36 GPT-5.5 only
2 Shared
6 Kimi K2.7 Code only
- Shared results
- 2
- GPT-5.5 only
- 36
- Kimi K2.7 Code only
- 6
- Like-for-like categories
- 1 / 8
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign 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.
Agentic
Directional only- GPT-5.5
- 61.0
- Supported · #17/152
- Kimi K2.7 Code
- 46.8
- Estimated · #72/152
- Basis
- BenchAlign lane · 13 vs 3 public rows
- Reading
- Directional only
Knowledge
Directional only- GPT-5.5
- 72.9
- Supported · #7/183
- Kimi K2.7 Code
- 61.6
- Estimated · #31/183
- Basis
- BenchAlign lane · 6 vs 0 public rows
- Reading
- Directional only
Instruction following
Directional only- GPT-5.5
- 93.2
- #7/123
- Kimi K2.7 Code
- 76.6
- #58/123
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.5
- 64.0
- #13/20
- Kimi K2.7 Code
- 75.5
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.5
- 69.6
- Unranked · 3 rankable rows
- Kimi K2.7 Code
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.5
- Not ranked
- Kimi K2.7 Code
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.5
- 71.3
- #19/48
- Kimi K2.7 Code
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.5 | Kimi K2.7 Code | Basis | Reading |
|---|---|---|---|---|
| Coding | 67.6Supported · #7/151 | 50.9Supported · #51/151 | Like-for-likeBenchAlign lane · 9 vs 5 public rows | GPT-5.5 leads · intervals overlap |
| Agentic | 61.0Supported · #17/152 | 46.8Estimated · #72/152 | Directional onlyBenchAlign lane · 13 vs 3 public rows | Directional only |
| Knowledge | 72.9Supported · #7/183 | 61.6Estimated · #31/183 | Directional onlyBenchAlign lane · 6 vs 0 public rows | Directional only |
| Instruction following | 93.2#7/123 | 76.6#58/123 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 64.0#13/20 | 75.5Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Math | 69.6Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 71.3#19/48 | 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) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
cursorBench32
Coding
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-5.5
- $0.02
- Fits in one request
- 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
- GPT-5.5
- $0.34
- Fits in one request
- 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
- GPT-5.5
- $0.5
- Fits in one request
- 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
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.
SpecificationGPT-5.5Kimi K2.7 Code
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.5
Kimi K2.7 Code
256K
API model ID
GPT-5.5
gpt-5.5
OpenAI pricingKimi K2.7 Code
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.5
$0.5 per 1M cached input tokens
OpenAI pricingKimi K2.7 Code
Not published
Documented inputs
GPT-5.5
Not sourced
Kimi K2.7 Code
Not sourced
Documented outputs
GPT-5.5
Not sourced
Kimi K2.7 Code
Not sourced
Provider availability
GPT-5.5
Not sourced
Kimi K2.7 Code
Not sourced
Reasoning profile
GPT-5.5
Reasoning
Kimi K2.7 Code
Reasoning
Weight access
GPT-5.5
Proprietary
Kimi K2.7 Code
Open Weight
License
GPT-5.5
Proprietary
Kimi K2.7 Code
Open Weight
Release date
GPT-5.5
2026-04-23
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
- GPT-5.5 has the higher public score estimate, 72.07 versus 65.49, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.34 vs $0.0595. Cache-heavy agent loop: $0.5 vs $0.249.
- Context tradeoff
- GPT-5.5 has the larger documented window (1M).
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.
GPT-5.5
API / mo$26,250
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
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence44 rows
Agentic
Terminal-Bench 2.0
GPT-5.582%SourceKimi K2.7 Code—Not directly comparable
CyberGym
GPT-5.581.8%SourceKimi K2.7 Code—Not directly comparable
BrowseComp
GPT-5.584.4%SourceKimi K2.7 Code—Not directly comparable
OSWorld-Verified
GPT-5.578.7%SourceKimi K2.7 Code—Not directly comparable
MCP Atlas
GPT-5.575.3%SourceKimi K2.7 Code76%SourceKimi K2.7 Code leads this result
Toolathlon
GPT-5.555.6%SourceKimi K2.7 Code—Not directly comparable
τ²-bench results
GPT-5.598%SourceKimi K2.7 Code—Not directly comparable
Gert Labs
GPT-5.572.93%SourceKimi K2.7 Code—Not directly comparable
ResearchClawBench
GPT-5.517.0%SourceKimi K2.7 Code—Not directly comparable
OSWorld 2.0
GPT-5.513.0%SourceKimi K2.7 Code—Not directly comparable
JobBench
GPT-5.542.7%SourceKimi K2.7 Code—Not directly comparable
ExploitGym
GPT-5.513.4%SourceKimi K2.7 Code—Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.576.4%SourceKimi K2.7 Code—Not directly comparable
Kimi Claw 24/7
GPT-5.5—Kimi K2.7 Code46.9%SourceNot directly comparable
MCP Mark Verified
GPT-5.5—Kimi K2.7 Code81.1%SourceNot directly comparable
Coding
SWE-bench Pro
GPT-5.558.6%SourceKimi K2.7 Code—Not directly comparable
Terminal-Bench 2.0
GPT-5.582.0%SourceKimi K2.7 Code—Not directly comparable
Vibe Code Bench
GPT-5.569.85%SourceKimi K2.7 Code—Not directly comparable
React Native Evals
GPT-5.584.7%SourceKimi K2.7 Code—Not directly comparable
cursorBench31
GPT-5.559.2%SourceKimi K2.7 Code—Not directly comparable
cursorBench32
Shared sourceGPT-5.558.4%Kimi K2.7 Code49.7%GPT-5.5 leads this result
FrontierCode 1.1 Main
GPT-5.543.0%SourceKimi K2.7 Code—Not directly comparable
LiveCodeBench (Vals)
GPT-5.585.3%SourceKimi K2.7 Code—Not directly comparable
SWE-bench (Vals)
GPT-5.582.6%SourceKimi K2.7 Code—Not directly comparable
Kimi Code Bench v2
GPT-5.5—Kimi K2.7 Code62.0%SourceNot directly comparable
ProgramBench
GPT-5.5—Kimi K2.7 Code53.6%SourceNot directly comparable
MLS-Bench Lite
GPT-5.5—Kimi K2.7 Code35.1%SourceNot directly comparable
OpenHarmony Bench
GPT-5.5—Kimi K2.7 Code52.1%SourceNot directly comparable
Reasoning
Knowledge
GPQA
GPT-5.593.6%SourceKimi K2.7 Code—Not directly comparable
GPQA-D
GPT-5.593.6%SourceKimi K2.7 Code—Not directly comparable
HLE
GPT-5.552.2%SourceKimi K2.7 Code—Not directly comparable
HLE w/o tools
GPT-5.541.4%SourceKimi K2.7 Code—Not directly comparable
GPQA Diamond (Vals)
GPT-5.593.2%SourceKimi K2.7 Code—Not directly comparable
MMLU-Pro (Vals)
GPT-5.588.1%SourceKimi K2.7 Code—Not directly comparable
Math
Multimodal
Frequently asked questions
Which is better, GPT-5.5 or Kimi K2.7 Code?
GPT-5.5 has the higher public score estimate, 72.07 versus 65.49, 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-5.5 or Kimi K2.7 Code?
GPT-5.5 leads the public coding lane, 67.6 to 50.9, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, GPT-5.5 or Kimi K2.7 Code?
GPT-5.5 scores higher for agentic tasks on the public lane, 61 to 46.8. Kimi K2.7 Code is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, GPT-5.5 or Kimi K2.7 Code?
For the stated presets, chat costs $0.02 on GPT-5.5 and $0.00295 on Kimi K2.7 Code; repository review costs $0.34 and $0.0595; the cache-heavy agent loop costs $0.5 and $0.249. Kimi K2.7 Code has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-5.5 or Kimi K2.7 Code?
GPT-5.5 has the larger documented context window: 1M, compared with 256K.
Related comparisons
Last updated September 10, 2026
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