Coding
Like-for-like- Kimi K2.7 Code
- 50.9
- Supported · #51/151
- Mistral Large 3
- 25.4
- Supported · #146/151
- Basis
- BenchAlign lane · 5 vs 0 public rows
- Reading
- Kimi K2.7 Code leads
Kimi K2.7 Code vs Mistral Large 3
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 25.4, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Chat turn cost
1K fresh input + 500 output tokens
Mistral Large 3
Mistral Large 3 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
Mistral Large 3
Mistral Large 3 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. Mistral Large 3 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
Mistral Large 3
Mistral Large 3 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 and Mistral Large 3 are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
8 Kimi K2.7 Code only
- Shared results
- 0
- Kimi K2.7 Code only
- 8
- Mistral Large 3 only
- 0
- 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- Kimi K2.7 Code
- 46.8
- Estimated · #72/152
- Mistral Large 3
- 39.8
- Estimated · #118/152
- Basis
- BenchAlign lane · 3 vs 0 public rows
- Reading
- Directional only
Knowledge
Directional only- Kimi K2.7 Code
- 61.6
- Estimated · #31/183
- Mistral Large 3
- 41.8
- Estimated · #126/183
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Directional only
Instruction following
Directional only- Kimi K2.7 Code
- 76.6
- #58/123
- Mistral Large 3
- 41.7
- #102/123
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Kimi K2.7 Code
- 75.5
- Unranked · 2 rankable rows
- Mistral Large 3
- 45.8
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Kimi K2.7 Code
- Not ranked
- Mistral Large 3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Kimi K2.7 Code
- Not ranked
- Mistral Large 3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Kimi K2.7 Code
- Not ranked
- Mistral Large 3
- 42.1
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Kimi K2.7 Code | Mistral Large 3 | Basis | Reading |
|---|---|---|---|---|
| Coding | 50.9Supported · #51/151 | 25.4Supported · #146/151 | Like-for-likeBenchAlign lane · 5 vs 0 public rows | Kimi K2.7 Code leads |
| Agentic | 46.8Estimated · #72/152 | 39.8Estimated · #118/152 | Directional onlyBenchAlign lane · 3 vs 0 public rows | Directional only |
| Knowledge | 61.6Estimated · #31/183 | 41.8Estimated · #126/183 | Directional onlyBenchAlign lane · 0 vs 0 public rows | Directional only |
| Instruction following | 76.6#58/123 | 41.7#102/123 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 75.5Unranked · 2 rankable rows | 45.8Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 42.1Unranked · 1 rankable row | 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
- Kimi K2.7 Code
- $0.00295
- Fits in one request
- Mistral Large 3
- $0.00125
- Fits in one request
Mistral Large 3 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Kimi K2.7 Code
- $0.0595
- Fits in one request
- Mistral Large 3
- $0.0295
- Fits in one request
Mistral Large 3 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Kimi K2.7 Code
- $0.249
- Fits in one request
- Cached input priced at the published list-input rate
- Mistral Large 3
- $0.125
- Fits in one request
- Cached input priced at the published list-input rate
Mistral Large 3 has the lower modeled cost
Kimi K2.7 Code has no published cached-input rate, so cached tokens use its listed input rate. Mistral Large 3 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.
SpecificationKimi K2.7 CodeMistral Large 3
Context window
Maximum documented context; output-token limits may be lower.
Kimi K2.7 Code
256K
Mistral Large 3
256K
API model ID
Kimi K2.7 Code
Not sourced
Mistral Large 3
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Kimi K2.7 Code
Not published
Mistral Large 3
Not published
Documented inputs
Kimi K2.7 Code
Not sourced
Mistral Large 3
Not sourced
Documented outputs
Kimi K2.7 Code
Not sourced
Mistral Large 3
Not sourced
Provider availability
Kimi K2.7 Code
Not sourced
Mistral Large 3
Not sourced
Reasoning profile
Kimi K2.7 Code
Reasoning
Mistral Large 3
Non-Reasoning
Weight access
Kimi K2.7 Code
Open Weight
Mistral Large 3
Proprietary
License
Kimi K2.7 Code
Open Weight
Mistral Large 3
Proprietary
Release date
Kimi K2.7 Code
2026-06-12
Mistral Large 3
2025-12-02
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: $0.0595 vs $0.0295. Cache-heavy agent loop: $0.249 vs $0.125.
- Context tradeoff
- Both models list 256K.
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.
Kimi K2.7 Code
API / mo$3,713
Self-host / mo$18,221
Break-even326M/day
Mistral Large 3
API / mo$1,500
Self-host / mo$9,110
Break-even380M/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 evidence8 rows
Agentic
Coding
Kimi Code Bench v2
Kimi K2.7 Code62.0%SourceMistral Large 3—Not directly comparable
ProgramBench
Kimi K2.7 Code53.6%SourceMistral Large 3—Not directly comparable
MLS-Bench Lite
Kimi K2.7 Code35.1%SourceMistral Large 3—Not directly comparable
cursorBench32
Kimi K2.7 Code49.7%SourceMistral Large 3—Not directly comparable
OpenHarmony Bench
Kimi K2.7 Code52.1%SourceMistral Large 3—Not directly comparable
Frequently asked questions
Which is better, Kimi K2.7 Code or Mistral Large 3?
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, Kimi K2.7 Code or Mistral Large 3?
Kimi K2.7 Code leads the public coding lane, 50.9 to 25.4, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, Kimi K2.7 Code or Mistral Large 3?
Kimi K2.7 Code scores higher for agentic tasks on the public lane, 46.8 to 39.8. Kimi K2.7 Code and Mistral Large 3 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, Kimi K2.7 Code or Mistral Large 3?
For the stated presets, chat costs $0.00295 on Kimi K2.7 Code and $0.00125 on Mistral Large 3; repository review costs $0.0595 and $0.0295; the cache-heavy agent loop costs $0.249 and $0.125. Kimi K2.7 Code has no published cached-input rate, so cached tokens use its listed input rate. Mistral Large 3 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Kimi K2.7 Code or Mistral Large 3?
Both models list the same context window, 256K.
Related comparisons
Last updated September 10, 2026
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