Agentic
Directional only- GPT-5 (high)
- 48.8
- Estimated · #61/152
- Mistral Medium 3.5 128B
- 21.9
- Supported · #150/152
- Basis
- BenchAlign lane · 1 vs 3 public rows
- Reading
- Directional only
GPT-5 (high) 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
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-5 (high)
GPT-5 (high) 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
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-5 (high)
GPT-5 (high) has the lower estimated token cost for this stated workload. GPT-5 (high) 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
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
GPT-5 (high)
GPT-5 (high) 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-5 (high) is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-5 (high) is 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.
2 GPT-5 (high) only
7 Mistral Medium 3.5 128B only
- Shared results
- 0
- GPT-5 (high) only
- 2
- Mistral Medium 3.5 128B only
- 7
- Like-for-like categories
- 0 / 8
1 category rests 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
Not comparable- GPT-5 (high)
- Not ranked
- Mistral Medium 3.5 128B
- 36.9
- Estimated · #127/151
- Basis
- BenchAlign lane · 1 vs 2 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5 (high)
- Not ranked
- Mistral Medium 3.5 128B
- 68.6
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5 (high)
- Not ranked
- Mistral Medium 3.5 128B
- 39.0
- Supported · #140/183
- Basis
- BenchAlign lane · 0 vs 2 public rows
- Reading
- Not comparable
Math
Not comparable- GPT-5 (high)
- Not ranked
- Mistral Medium 3.5 128B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5 (high)
- Not ranked
- Mistral Medium 3.5 128B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5 (high)
- 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- GPT-5 (high)
- Not ranked
- Mistral Medium 3.5 128B
- 84.0
- #48/123
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5 (high) | Mistral Medium 3.5 128B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 48.8Estimated · #61/152 | 21.9Supported · #150/152 | Directional onlyBenchAlign lane · 1 vs 3 public rows | Directional only |
| Coding | Not ranked | 36.9Estimated · #127/151 | Not comparableBenchAlign lane · 1 vs 2 public rows | Not comparable |
| Reasoning | Not ranked | 68.6Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | 39.0Supported · #140/183 | Not comparableBenchAlign lane · 0 vs 2 public 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 | 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.
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 (high)
- $0.00625
- 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
- GPT-5 (high)
- $0.0925
- Fits in one request
- Mistral Medium 3.5 128B
- $0.0975
- Fits in one request
GPT-5 (high) 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 (high)
- $0.375
- Fits 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
GPT-5 (high) has the lower modeled cost
GPT-5 (high) 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.
SpecificationGPT-5 (high)Mistral Medium 3.5 128B
Context window
Maximum documented context; output-token limits may be lower.
GPT-5 (high)
400K
Mistral Medium 3.5 128B
256K
API model ID
GPT-5 (high)
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.
GPT-5 (high)
Not published
Mistral Medium 3.5 128B
Not published
Documented inputs
GPT-5 (high)
Not sourced
Mistral Medium 3.5 128B
Not sourced
Documented outputs
GPT-5 (high)
Not sourced
Mistral Medium 3.5 128B
Not sourced
Provider availability
GPT-5 (high)
Not sourced
Mistral Medium 3.5 128B
Not sourced
Reasoning profile
GPT-5 (high)
Reasoning
Mistral Medium 3.5 128B
Reasoning
Weight access
GPT-5 (high)
Proprietary
Mistral Medium 3.5 128B
Open Weight
License
GPT-5 (high)
Proprietary
Mistral Medium 3.5 128B
Open Weight
Release date
GPT-5 (high)
2025-08-07
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
- 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 $0.0975. Cache-heavy agent loop: $0.375 vs $0.405.
- Context tradeoff
- GPT-5 (high) has the larger documented window (400K).
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 evidence9 rows
Agentic
JobBench
GPT-5 (high)8.5%SourceMistral Medium 3.5 128B—Not directly comparable
τ³-bench results
GPT-5 (high)—Mistral Medium 3.5 128B91.4%SourceNot directly comparable
Gert Labs
GPT-5 (high)—Mistral Medium 3.5 128B39.10%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5 (high)—Mistral Medium 3.5 128B39.0%SourceNot directly comparable
Coding
Knowledge
Frequently asked questions
Which is better, GPT-5 (high) or Mistral Medium 3.5 128B?
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 (high) or Mistral Medium 3.5 128B?
GPT-5 (high) is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT-5 (high) or Mistral Medium 3.5 128B?
GPT-5 (high) scores higher for agentic tasks on the public lane, 48.8 to 21.9. GPT-5 (high) is 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 (high) or Mistral Medium 3.5 128B?
For the stated presets, chat costs $0.00625 on GPT-5 (high) and $0.00525 on Mistral Medium 3.5 128B; repository review costs $0.0925 and $0.0975; the cache-heavy agent loop costs $0.375 and $0.405. GPT-5 (high) 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, GPT-5 (high) or Mistral Medium 3.5 128B?
GPT-5 (high) has the larger documented context window: 400K, compared with 256K.
Related comparisons
Last updated September 10, 2026
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