Agentic
Directional only- GPT-4.1 nano
- 33.4
- Estimated · #136/152
- Kimi K2.7 Code
- 46.8
- Estimated · #72/152
- Basis
- BenchAlign lane · 0 vs 3 public rows
- Reading
- Directional only
GPT-4.1 nano 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.
Long documents
Prompts that approach the documented context limit
GPT-4.1 nano
GPT-4.1 nano has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
GPT-4.1 nano
GPT-4.1 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-4.1 nano
GPT-4.1 nano has the lower estimated token cost for this stated workload. GPT-4.1 nano 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
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
GPT-4.1 nano
GPT-4.1 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
GPT-4.1 nano is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-4.1 nano 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-4.1 nano only
8 Kimi K2.7 Code only
- Shared results
- 0
- GPT-4.1 nano only
- 4
- Kimi K2.7 Code only
- 8
- Like-for-like categories
- 0 / 8
4 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.
Coding
Directional only- GPT-4.1 nano
- 30.9
- Estimated · #136/151
- Kimi K2.7 Code
- 50.9
- Supported · #51/151
- Basis
- BenchAlign lane · 0 vs 5 public rows
- Reading
- Directional only
Knowledge
Directional only- GPT-4.1 nano
- 29.6
- Supported · #177/183
- Kimi K2.7 Code
- 61.6
- Estimated · #31/183
- Basis
- BenchAlign lane · 2 vs 0 public rows
- Reading
- Directional only
Instruction following
Directional only- GPT-4.1 nano
- 36.3
- #108/123
- Kimi K2.7 Code
- 76.6
- #58/123
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-4.1 nano
- 35.0
- 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-4.1 nano
- 25.6
- Unranked · 1 rankable row
- Kimi K2.7 Code
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-4.1 nano
- Not ranked
- Kimi K2.7 Code
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-4.1 nano
- 25.3
- Unranked · 1 rankable row
- Kimi K2.7 Code
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-4.1 nano | Kimi K2.7 Code | Basis | Reading |
|---|---|---|---|---|
| Agentic | 33.4Estimated · #136/152 | 46.8Estimated · #72/152 | Directional onlyBenchAlign lane · 0 vs 3 public rows | Directional only |
| Coding | 30.9Estimated · #136/151 | 50.9Supported · #51/151 | Directional onlyBenchAlign lane · 0 vs 5 public rows | Directional only |
| Knowledge | 29.6Supported · #177/183 | 61.6Estimated · #31/183 | Directional onlyBenchAlign lane · 2 vs 0 public rows | Directional only |
| Instruction following | 36.3#108/123 | 76.6#58/123 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 35.0Unranked · 2 rankable rows | 75.5Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 25.6Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 25.3Unranked · 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-4.1 nano
- $0.0003
- Fits in one request
- Kimi K2.7 Code
- $0.00295
- Fits in one request
GPT-4.1 nano has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-4.1 nano
- $0.0062
- Fits in one request
- Kimi K2.7 Code
- $0.0595
- Fits in one request
GPT-4.1 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-4.1 nano
- $0.026
- 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
GPT-4.1 nano has the lower modeled cost
GPT-4.1 nano 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-4.1 nanoKimi K2.7 Code
Context window
Maximum documented context; output-token limits may be lower.
GPT-4.1 nano
1M
Kimi K2.7 Code
256K
API model ID
GPT-4.1 nano
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-4.1 nano
Not published
Kimi K2.7 Code
Not published
Documented inputs
GPT-4.1 nano
Not sourced
Kimi K2.7 Code
Not sourced
Documented outputs
GPT-4.1 nano
Not sourced
Kimi K2.7 Code
Not sourced
Provider availability
GPT-4.1 nano
Not sourced
Kimi K2.7 Code
Not sourced
Reasoning profile
GPT-4.1 nano
Non-Reasoning
Kimi K2.7 Code
Reasoning
Weight access
GPT-4.1 nano
Proprietary
Kimi K2.7 Code
Open Weight
License
GPT-4.1 nano
Proprietary
Kimi K2.7 Code
Open Weight
Release date
GPT-4.1 nano
2025-04-14
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: $0.0062 vs $0.0595. Cache-heavy agent loop: $0.026 vs $0.249.
- Context tradeoff
- GPT-4.1 nano 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-4.1 nano
API / mo$375
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
Kimi Code Bench v2
GPT-4.1 nano—Kimi K2.7 Code62.0%SourceNot directly comparable
ProgramBench
GPT-4.1 nano—Kimi K2.7 Code53.6%SourceNot directly comparable
MLS-Bench Lite
GPT-4.1 nano—Kimi K2.7 Code35.1%SourceNot directly comparable
cursorBench32
GPT-4.1 nano—Kimi K2.7 Code49.7%SourceNot directly comparable
OpenHarmony Bench
GPT-4.1 nano—Kimi K2.7 Code52.1%SourceNot directly comparable
Knowledge
Math
FrontierMath v2 (Tiers 1-3)
GPT-4.1 nano1.034%SourceKimi K2.7 Code—Not directly comparable
Instruction following
IFEval
GPT-4.1 nano83.2%SourceKimi K2.7 Code—Not directly comparable
Frequently asked questions
Which is better, GPT-4.1 nano 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-4.1 nano or Kimi K2.7 Code?
Kimi K2.7 Code scores higher for coding on the public lane, 50.9 to 30.9. GPT-4.1 nano is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, GPT-4.1 nano or Kimi K2.7 Code?
Kimi K2.7 Code scores higher for agentic tasks on the public lane, 46.8 to 33.4. GPT-4.1 nano 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-4.1 nano or Kimi K2.7 Code?
For the stated presets, chat costs $0.0003 on GPT-4.1 nano and $0.00295 on Kimi K2.7 Code; repository review costs $0.0062 and $0.0595; the cache-heavy agent loop costs $0.026 and $0.249. GPT-4.1 nano 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-4.1 nano or Kimi K2.7 Code?
GPT-4.1 nano has the larger documented context window: 1M, compared with 256K.
Related comparisons
Last updated September 10, 2026
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