Agentic
Like-for-like- Claude Sonnet 4.6
- 44.0
- Supported · #93/152
- Mistral Medium 3.5 128B
- 21.9
- Supported · #150/152
- Basis
- BenchAlign lane · 8 vs 3 public rows
- Reading
- Claude Sonnet 4.6 leads
Claude Sonnet 4.6 vs Mistral Medium 3.5 128B
Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Claude Sonnet 4.6 has the higher public score, 62.96 versus 30.13, and the 90% score intervals do not overlap.
6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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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.
Agentic work
Tool use, computer use, and multi-step task completion
Claude Sonnet 4.6
Claude Sonnet 4.6 leads on the public agentic lane, 44 to 21.9, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Long documents
Prompts that approach the documented context limit
Mistral Medium 3.5 128B
Mistral Medium 3.5 128B has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Mistral Medium 3.5 128B
Mistral Medium 3.5 128B 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
Mistral Medium 3.5 128B
Mistral Medium 3.5 128B 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
Mistral Medium 3.5 128B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Claude Sonnet 4.6 does not fit this workload in one request. Claude Sonnet 4.6 has no published cached-input rate, so cached tokens use its listed input rate. Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
19 Claude Sonnet 4.6 only
6 Shared
1 Mistral Medium 3.5 128B only
- Shared results
- 6
- Claude Sonnet 4.6 only
- 19
- Mistral Medium 3.5 128B only
- 1
- Like-for-like categories
- 2 / 8
2 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.
Knowledge
Like-for-like- Claude Sonnet 4.6
- 55.8
- Supported · #51/183
- Mistral Medium 3.5 128B
- 39.0
- Supported · #140/183
- Basis
- BenchAlign lane · 6 vs 2 public rows
- Reading
- Claude Sonnet 4.6 leads · intervals overlap
Coding
Directional only- Claude Sonnet 4.6
- 52.2
- Supported · #48/151
- Mistral Medium 3.5 128B
- 36.9
- Estimated · #127/151
- Basis
- BenchAlign lane · 8 vs 2 public rows
- Reading
- Directional only
Instruction following
Directional only- Claude Sonnet 4.6
- 48.2
- #86/123
- Mistral Medium 3.5 128B
- 84.0
- #48/123
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Claude Sonnet 4.6
- 67.9
- Unranked · 2 rankable rows
- Mistral Medium 3.5 128B
- 68.6
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Sonnet 4.6
- 49.0
- Unranked · 2 rankable rows
- Mistral Medium 3.5 128B
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Sonnet 4.6
- Not ranked
- Mistral Medium 3.5 128B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Sonnet 4.6
- 54.1
- #33/48
- Mistral Medium 3.5 128B
- 55.6
- Unranked · 1 rankable row
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Sonnet 4.6 | Mistral Medium 3.5 128B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 44.0Supported · #93/152 | 21.9Supported · #150/152 | Like-for-likeBenchAlign lane · 8 vs 3 public rows | Claude Sonnet 4.6 leads |
| Knowledge | 55.8Supported · #51/183 | 39.0Supported · #140/183 | Like-for-likeBenchAlign lane · 6 vs 2 public rows | Claude Sonnet 4.6 leads · intervals overlap |
| Coding | 52.2Supported · #48/151 | 36.9Estimated · #127/151 | Directional onlyBenchAlign lane · 8 vs 2 public rows | Directional only |
| Instruction following | 48.2#86/123 | 84.0#48/123 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 67.9Unranked · 2 rankable rows | 68.6Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 49.0Unranked · 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 | 54.1#33/48 | 55.6Unranked · 1 rankable row | Not comparableProvisional lane · 1 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.
MMLU-Pro (Vals)
Knowledge
Claude Sonnet 4.6: 87.3%Mistral Medium 3.5 128B: 75.3%Normalized gap 12.0Claude Sonnet 4.6 source Mistral Medium 3.5 128B sourceSWE-bench Verified
Coding
Claude Sonnet 4.6: 79.6%Mistral Medium 3.5 128B: 77.6%Normalized gap 2.0Claude Sonnet 4.6 source Mistral Medium 3.5 128B source
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
- Claude Sonnet 4.6
- $0.0105
- Fits in one request
- Mistral Medium 3.5 128B
- $0.00525
- Fits in one request
Mistral Medium 3.5 128B has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Sonnet 4.6
- $0.195
- Fits in one request
- Mistral Medium 3.5 128B
- $0.0975
- Fits in one request
Mistral Medium 3.5 128B has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Claude Sonnet 4.6
- $0.81
- Does not fit in one request
- Cached input priced at the published list-input rate
- Mistral Medium 3.5 128B
- $0.405
- Fits in one request
- Cached input priced at the published list-input rate
Claude Sonnet 4.6 does not fit this workload in one request. Claude Sonnet 4.6 has no published cached-input rate, so cached tokens use its listed input rate. Mistral Medium 3.5 128B 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.
SpecificationClaude Sonnet 4.6Mistral Medium 3.5 128B
Context window
Maximum documented context; output-token limits may be lower.
Claude Sonnet 4.6
200K
Mistral Medium 3.5 128B
256K
API model ID
Claude Sonnet 4.6
Not sourced
Mistral Medium 3.5 128B
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Sonnet 4.6
Not published
Mistral Medium 3.5 128B
Not published
Documented inputs
Claude Sonnet 4.6
Not sourced
Mistral Medium 3.5 128B
Not sourced
Documented outputs
Claude Sonnet 4.6
Not sourced
Mistral Medium 3.5 128B
Not sourced
Provider availability
Claude Sonnet 4.6
Not sourced
Mistral Medium 3.5 128B
Not sourced
Reasoning profile
Claude Sonnet 4.6
Non-Reasoning
Mistral Medium 3.5 128B
Reasoning
Weight access
Claude Sonnet 4.6
Proprietary
Mistral Medium 3.5 128B
Open Weight
License
Claude Sonnet 4.6
Proprietary
Mistral Medium 3.5 128B
Open Weight
Release date
Claude Sonnet 4.6
2026-02-01
Mistral Medium 3.5 128B
2026-04-29
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
- Claude Sonnet 4.6 has the higher public score, 62.96 versus 30.13, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.195 vs $0.0975. Cache-heavy agent loop: $0.81 vs $0.405.
- Context tradeoff
- Mistral Medium 3.5 128B has the larger documented window (256K).
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence26 rows
Agentic
Terminal-Bench 2.0
Claude Sonnet 4.659.1%SourceMistral Medium 3.5 128B—Not directly comparable
OSWorld-Verified
Claude Sonnet 4.672.1%SourceMistral Medium 3.5 128B—Not directly comparable
Claw-Eval
Claude Sonnet 4.667.8%SourceMistral Medium 3.5 128B—Not directly comparable
CyberGym
Claude Sonnet 4.665.2%SourceMistral Medium 3.5 128B—Not directly comparable
Gert Labs
Shared sourceClaude Sonnet 4.662.92%Mistral Medium 3.5 128B39.10%Claude Sonnet 4.6 leads this result
OSWorld 2.0
Claude Sonnet 4.68.3%SourceMistral Medium 3.5 128B—Not directly comparable
JobBench
Claude Sonnet 4.636.9%SourceMistral Medium 3.5 128B—Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Sonnet 4.657.3%SourceMistral Medium 3.5 128B39.0%SourceClaude Sonnet 4.6 leads this result
τ³-bench results
Claude Sonnet 4.6—Mistral Medium 3.5 128B91.4%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Sonnet 4.679.6%SourceMistral Medium 3.5 128B77.6%SourceClaude Sonnet 4.6 leads this result
SWE-Rebench
Claude Sonnet 4.660.7%SourceMistral Medium 3.5 128B—Not directly comparable
React Native Evals
Claude Sonnet 4.680.6%SourceMistral Medium 3.5 128B—Not directly comparable
Vibe Code Bench
Claude Sonnet 4.651.48%SourceMistral Medium 3.5 128B—Not directly comparable
cursorBench31
Claude Sonnet 4.648.8%SourceMistral Medium 3.5 128B—Not directly comparable
FrontierCode 1.1 Main
Claude Sonnet 4.624.3%SourceMistral Medium 3.5 128B—Not directly comparable
LiveCodeBench (Vals)
Claude Sonnet 4.682.1%SourceMistral Medium 3.5 128B—Not directly comparable
SWE-bench (Vals)
Claude Sonnet 4.677.4%SourceMistral Medium 3.5 128B66.4%SourceClaude Sonnet 4.6 leads this result
Knowledge
GPQA
Claude Sonnet 4.689.9%SourceMistral Medium 3.5 128B—Not directly comparable
SuperGPQA
Claude Sonnet 4.695%SourceMistral Medium 3.5 128B—Not directly comparable
MMLU-Pro
Claude Sonnet 4.679.2%SourceMistral Medium 3.5 128B—Not directly comparable
HLE
Claude Sonnet 4.649%SourceMistral Medium 3.5 128B—Not directly comparable
GPQA Diamond (Vals)
Claude Sonnet 4.685.6%SourceMistral Medium 3.5 128B34.8%SourceClaude Sonnet 4.6 leads this result
MMLU-Pro (Vals)
Claude Sonnet 4.687.3%SourceMistral Medium 3.5 128B75.3%SourceClaude Sonnet 4.6 leads this result
Math
Multimodal
CharXiv
Claude Sonnet 4.677.4%SourceMistral Medium 3.5 128B—Not directly comparable
Frequently asked questions
Which is better, Claude Sonnet 4.6 or Mistral Medium 3.5 128B?
Claude Sonnet 4.6 has the higher public score, 62.96 versus 30.13, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, Claude Sonnet 4.6 or Mistral Medium 3.5 128B?
Claude Sonnet 4.6 scores higher for coding on the public lane, 52.2 to 36.9. Mistral Medium 3.5 128B 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, Claude Sonnet 4.6 or Mistral Medium 3.5 128B?
Claude Sonnet 4.6 leads the public agentic tasks lane, 44 to 21.9, with Supported evidence for both models and non-overlapping 90% intervals.
Which costs less, Claude Sonnet 4.6 or Mistral Medium 3.5 128B?
For the stated presets, chat costs $0.0105 on Claude Sonnet 4.6 and $0.00525 on Mistral Medium 3.5 128B; repository review costs $0.195 and $0.0975; the cache-heavy agent loop costs $0.81 and $0.405. Claude Sonnet 4.6 does not fit this workload in one request. Claude Sonnet 4.6 has no published cached-input rate, so cached tokens use its listed input rate. Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Claude Sonnet 4.6 or Mistral Medium 3.5 128B?
Mistral Medium 3.5 128B has the larger documented context window: 256K, compared with 200K.
Related comparisons
Last updated September 10, 2026
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