Agentic
Like-for-like- Claude Haiku 4.5
- 27.0
- Supported · #142/152
- Mistral Medium 3.5 128B
- 21.9
- Supported · #150/152
- Basis
- BenchAlign lane · 2 vs 3 public rows
- Reading
- Claude Haiku 4.5 leads · intervals overlap
Claude Haiku 4.5 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 Haiku 4.5 has the higher public score estimate, 52.79 versus 30.13, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
5 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.
Agentic work
Tool use, computer use, and multi-step task completion
Claude Haiku 4.5
Claude Haiku 4.5 leads on the public agentic lane, 27 to 21.9, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
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
Claude Haiku 4.5
Claude Haiku 4.5 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
Claude Haiku 4.5
Claude Haiku 4.5 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 Haiku 4.5 does not fit this workload in one request. 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.
5 Claude Haiku 4.5 only
5 Shared
2 Mistral Medium 3.5 128B only
- Shared results
- 5
- Claude Haiku 4.5 only
- 5
- Mistral Medium 3.5 128B only
- 2
- Like-for-like categories
- 1 / 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.
Coding
Directional only- Claude Haiku 4.5
- 27.2
- Supported · #144/151
- Mistral Medium 3.5 128B
- 36.9
- Estimated · #127/151
- Basis
- BenchAlign lane · 4 vs 2 public rows
- Reading
- Directional only
Knowledge
Directional only- Claude Haiku 4.5
- 44.2
- Estimated · #115/183
- Mistral Medium 3.5 128B
- 39.0
- Supported · #140/183
- Basis
- BenchAlign lane · 2 vs 2 public rows
- Reading
- Directional only
Reasoning
Not comparable- Claude Haiku 4.5
- Not ranked
- 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 Haiku 4.5
- 28.9
- 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 Haiku 4.5
- Not ranked
- Mistral Medium 3.5 128B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Haiku 4.5
- Not ranked
- Mistral Medium 3.5 128B
- 55.6
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Haiku 4.5
- Not ranked
- Mistral Medium 3.5 128B
- 84.0
- #48/123
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Haiku 4.5 | Mistral Medium 3.5 128B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 27.0Supported · #142/152 | 21.9Supported · #150/152 | Like-for-likeBenchAlign lane · 2 vs 3 public rows | Claude Haiku 4.5 leads · intervals overlap |
| Coding | 27.2Supported · #144/151 | 36.9Estimated · #127/151 | Directional onlyBenchAlign lane · 4 vs 2 public rows | Directional only |
| Knowledge | 44.2Estimated · #115/183 | 39.0Supported · #140/183 | Directional onlyBenchAlign lane · 2 vs 2 public rows | Directional only |
| Reasoning | Not ranked | 68.6Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 28.9Unranked · 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 | Not ranked | 55.6Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 84.0#48/123 | 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.
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.
SWE-bench Verified
Coding
Claude Haiku 4.5: 73.3%Mistral Medium 3.5 128B: 77.6%Normalized gap 4.3Claude Haiku 4.5 source Mistral Medium 3.5 128B sourceMMLU-Pro (Vals)
Knowledge
Claude Haiku 4.5: 78.7%Mistral Medium 3.5 128B: 75.3%Normalized gap 3.4Claude Haiku 4.5 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 Haiku 4.5
- $0.0035
- Fits in one request
- Mistral Medium 3.5 128B
- $0.00525
- Fits in one request
Claude Haiku 4.5 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Haiku 4.5
- $0.065
- Fits in one request
- Mistral Medium 3.5 128B
- $0.0975
- Fits in one request
Claude Haiku 4.5 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 Haiku 4.5
- $0.09
- Does not fit in one request
- Mistral Medium 3.5 128B
- $0.405
- Fits in one request
- Cached input priced at the published list-input rate
Claude Haiku 4.5 does not fit this workload in one request. 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 Haiku 4.5Mistral Medium 3.5 128B
Context window
Maximum documented context; output-token limits may be lower.
Claude Haiku 4.5
Mistral Medium 3.5 128B
256K
API model ID
Claude Haiku 4.5
claude-haiku-4-5-20251001
Claude API pricingMistral 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 Haiku 4.5
$0.1 per 1M cached input tokens
Claude API pricingMistral Medium 3.5 128B
Not published
Documented inputs
Claude Haiku 4.5
Not sourced
Mistral Medium 3.5 128B
Not sourced
Documented outputs
Claude Haiku 4.5
Not sourced
Mistral Medium 3.5 128B
Not sourced
Provider availability
Claude Haiku 4.5
Not sourced
Mistral Medium 3.5 128B
Not sourced
Reasoning profile
Claude Haiku 4.5
Non-Reasoning
Mistral Medium 3.5 128B
Reasoning
Weight access
Claude Haiku 4.5
Proprietary
Mistral Medium 3.5 128B
Open Weight
License
Claude Haiku 4.5
Proprietary
Mistral Medium 3.5 128B
Open Weight
Release date
Claude Haiku 4.5
2025-10-15
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 Haiku 4.5 has the higher public score estimate, 52.79 versus 30.13, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.065 vs $0.0975. Cache-heavy agent loop: $0.09 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 evidence12 rows
Agentic
JobBench
Claude Haiku 4.516.0%SourceMistral Medium 3.5 128B—Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Haiku 4.543.8%SourceMistral Medium 3.5 128B39.0%SourceClaude Haiku 4.5 leads this result
τ³-bench results
Claude Haiku 4.5—Mistral Medium 3.5 128B91.4%SourceNot directly comparable
Gert Labs
Claude Haiku 4.5—Mistral Medium 3.5 128B39.10%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Haiku 4.573.3%SourceMistral Medium 3.5 128B77.6%SourceMistral Medium 3.5 128B leads this result
VulcanBench v3
Claude Haiku 4.576.2%SourceMistral Medium 3.5 128B—Not directly comparable
LiveCodeBench (Vals)
Claude Haiku 4.541.2%SourceMistral Medium 3.5 128B—Not directly comparable
SWE-bench (Vals)
Claude Haiku 4.566.6%SourceMistral Medium 3.5 128B66.4%SourceClaude Haiku 4.5 leads this result
Knowledge
Math
Frequently asked questions
Which is better, Claude Haiku 4.5 or Mistral Medium 3.5 128B?
Claude Haiku 4.5 has the higher public score estimate, 52.79 versus 30.13, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Claude Haiku 4.5 or Mistral Medium 3.5 128B?
Mistral Medium 3.5 128B scores higher for coding on the public lane, 36.9 to 27.2. 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 Haiku 4.5 or Mistral Medium 3.5 128B?
Claude Haiku 4.5 leads the public agentic tasks lane, 27 to 21.9, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, Claude Haiku 4.5 or Mistral Medium 3.5 128B?
For the stated presets, chat costs $0.0035 on Claude Haiku 4.5 and $0.00525 on Mistral Medium 3.5 128B; repository review costs $0.065 and $0.0975; the cache-heavy agent loop costs $0.09 and $0.405. Claude Haiku 4.5 does not fit this workload in one request. 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 Haiku 4.5 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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