Coding
Like-for-like- GPT-5.1
- 45.0
- Supported · #89/151
- Kimi K2.7 Code
- 50.9
- Supported · #51/151
- Basis
- BenchAlign lane · 1 vs 5 public rows
- Reading
- Kimi K2.7 Code leads · intervals overlap
GPT-5.1 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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.
0 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
Kimi K2.7 Code
Kimi K2.7 Code leads on the public coding lane, 50.9 to 45, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Long documents
Prompts that approach the documented context limit
GPT-5.1
GPT-5.1 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. GPT-5.1 has no published cached-input rate, so cached tokens use its listed input rate. 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
GPT-5.1 and Kimi K2.7 Code are 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.
4 GPT-5.1 only
8 Kimi K2.7 Code only
- Shared results
- 0
- GPT-5.1 only
- 4
- Kimi K2.7 Code only
- 8
- 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-5.1
- 59.4
- Estimated · #24/152
- Kimi K2.7 Code
- 46.8
- Estimated · #72/152
- Basis
- BenchAlign lane · 1 vs 3 public rows
- Reading
- Directional only
Knowledge
Directional only- GPT-5.1
- 58.1
- Estimated · #41/183
- Kimi K2.7 Code
- 61.6
- Estimated · #31/183
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Directional only
Instruction following
Directional only- GPT-5.1
- 89.3
- #26/123
- Kimi K2.7 Code
- 76.6
- #58/123
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.1
- 76.0
- Unranked · 2 rankable rows
- Kimi K2.7 Code
- 75.5
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.1
- 49.2
- Unranked · 2 rankable rows
- Kimi K2.7 Code
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.1
- Not ranked
- Kimi K2.7 Code
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.1
- 71.2
- Unranked · 1 rankable row
- Kimi K2.7 Code
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.1 | Kimi K2.7 Code | Basis | Reading |
|---|---|---|---|---|
| Coding | 45.0Supported · #89/151 | 50.9Supported · #51/151 | Like-for-likeBenchAlign lane · 1 vs 5 public rows | Kimi K2.7 Code leads · intervals overlap |
| Agentic | 59.4Estimated · #24/152 | 46.8Estimated · #72/152 | Directional onlyBenchAlign lane · 1 vs 3 public rows | Directional only |
| Knowledge | 58.1Estimated · #41/183 | 61.6Estimated · #31/183 | Directional onlyBenchAlign lane · 0 vs 0 public rows | Directional only |
| Instruction following | 89.3#26/123 | 76.6#58/123 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 76.0Unranked · 2 rankable rows | 75.5Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 49.2Unranked · 2 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 | 71.2Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 0 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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-5.1
- $0.00625
- 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-5.1
- $0.0925
- 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-5.1
- $0.375
- Fits in one request
- Cached input priced at the published list-input rate
- 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
GPT-5.1 has no published cached-input rate, so cached tokens use its listed input rate. 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-5.1Kimi K2.7 Code
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.1
400K
Kimi K2.7 Code
256K
API model ID
GPT-5.1
Not sourced
Kimi 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-5.1
Not published
Kimi K2.7 Code
Not published
Documented inputs
GPT-5.1
Not sourced
Kimi K2.7 Code
Not sourced
Documented outputs
GPT-5.1
Not sourced
Kimi K2.7 Code
Not sourced
Provider availability
GPT-5.1
Not sourced
Kimi K2.7 Code
Not sourced
Reasoning profile
GPT-5.1
Reasoning
Kimi K2.7 Code
Reasoning
Weight access
GPT-5.1
Proprietary
Kimi K2.7 Code
Open Weight
License
GPT-5.1
Proprietary
Kimi K2.7 Code
Open Weight
Release date
GPT-5.1
2025-11-13
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- Repository review: $0.0925 vs $0.0595. Cache-heavy agent loop: $0.375 vs $0.249.
- Context tradeoff
- GPT-5.1 has the larger documented window (400K).
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-5.1
API / mo$8,438
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 evidence12 rows
Agentic
Coding
Vibe Code Bench
GPT-5.124.61%SourceKimi K2.7 Code—Not directly comparable
Kimi Code Bench v2
GPT-5.1—Kimi K2.7 Code62.0%SourceNot directly comparable
ProgramBench
GPT-5.1—Kimi K2.7 Code53.6%SourceNot directly comparable
MLS-Bench Lite
GPT-5.1—Kimi K2.7 Code35.1%SourceNot directly comparable
cursorBench32
GPT-5.1—Kimi K2.7 Code49.7%SourceNot directly comparable
OpenHarmony Bench
GPT-5.1—Kimi K2.7 Code52.1%SourceNot directly comparable
Math
Frequently asked questions
Which is better, GPT-5.1 or Kimi K2.7 Code?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-5.1 or Kimi K2.7 Code?
Kimi K2.7 Code leads the public coding lane, 50.9 to 45, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, GPT-5.1 or Kimi K2.7 Code?
GPT-5.1 scores higher for agentic tasks on the public lane, 59.4 to 46.8. GPT-5.1 and Kimi K2.7 Code are 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-5.1 or Kimi K2.7 Code?
For the stated presets, chat costs $0.00625 on GPT-5.1 and $0.00295 on Kimi K2.7 Code; repository review costs $0.0925 and $0.0595; the cache-heavy agent loop costs $0.375 and $0.249. GPT-5.1 has no published cached-input rate, so cached tokens use its listed input rate. 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-5.1 or Kimi K2.7 Code?
GPT-5.1 has the larger documented context window: 400K, compared with 256K.
Related comparisons
Last updated September 10, 2026
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