Coding
Directional only- GPT-5.2
- 70.6
- Mistral Medium 3.5 128B
- 77.6
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
GPT-5.2 vs Mistral Medium 3.5 128B
Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
2 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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-5.2
GPT-5.2 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
Mistral Medium 3.5 128B
Mistral Medium 3.5 128B has the lower estimated token cost for this stated workload. GPT-5.2 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
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
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
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.
13 GPT-5.2 only
2 Shared
2 Mistral Medium 3.5 128B only
- Shared results
- 2
- GPT-5.2 only
- 13
- Mistral Medium 3.5 128B only
- 2
- Like-for-like categories
- 0 / 8
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Agentic
Not comparable- GPT-5.2
- 55.7
- Mistral Medium 3.5 128B
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.2
- 52.9
- Mistral Medium 3.5 128B
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5.2
- 92.4
- Mistral Medium 3.5 128B
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.2
- 35.2
- Mistral Medium 3.5 128B
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.2
- Not measured
- Mistral Medium 3.5 128B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.2
- 80.4
- Mistral Medium 3.5 128B
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.2
- Not measured
- Mistral Medium 3.5 128B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GPT-5.2 | Mistral Medium 3.5 128B | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 70.6 | 77.6 | Directional only2 vs 1 rows | Directional only |
| Agentic | 55.7 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Reasoning | 52.9 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Knowledge | 92.4 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Math | 35.2 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | 80.4 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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
GPT-5.2: 80%Mistral Medium 3.5 128B: 77.6%Normalized gap 2.4GPT-5.2 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
- GPT-5.2
- $0.00875
- 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-5.2
- $0.1295
- 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
- GPT-5.2
- $0.525
- 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
Mistral Medium 3.5 128B has the lower modeled cost
GPT-5.2 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-5.2Mistral Medium 3.5 128B
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.2
400K
Mistral Medium 3.5 128B
256K
API model ID
GPT-5.2
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-5.2
Not published
Mistral Medium 3.5 128B
Not published
Documented inputs
GPT-5.2
Not sourced
Mistral Medium 3.5 128B
Not sourced
Documented outputs
GPT-5.2
Not sourced
Mistral Medium 3.5 128B
Not sourced
Provider availability
GPT-5.2
Not sourced
Mistral Medium 3.5 128B
Not sourced
Reasoning profile
GPT-5.2
Reasoning
Mistral Medium 3.5 128B
Reasoning
Weight access
GPT-5.2
Proprietary
Mistral Medium 3.5 128B
Open Weight
License
GPT-5.2
Proprietary
Mistral Medium 3.5 128B
Open Weight
Release date
GPT-5.2
2025-12-11
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- Repository review: $0.1295 vs $0.0975. Cache-heavy agent loop: $0.525 vs $0.405.
- Context tradeoff
- GPT-5.2 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 evidence17 rows
Agentic
BrowseComp
GPT-5.265.8%SourceMistral Medium 3.5 128B—Not directly comparable
OSWorld-Verified
GPT-5.247.3%SourceMistral Medium 3.5 128B—Not directly comparable
Gert Labs
Shared sourceGPT-5.246.54%Mistral Medium 3.5 128B39.10%GPT-5.2 leads this result
JobBench
GPT-5.234.3%SourceMistral Medium 3.5 128B—Not directly comparable
τ³-bench results
GPT-5.2—Mistral Medium 3.5 128B91.4%SourceNot directly comparable
Coding
SWE-bench Verified
GPT-5.280%SourceMistral Medium 3.5 128B77.6%SourceGPT-5.2 leads this result
SWE-bench Pro
GPT-5.255.6%SourceMistral Medium 3.5 128B—Not directly comparable
Vibe Code Bench
GPT-5.253.50%SourceMistral Medium 3.5 128B—Not directly comparable
EEBench
GPT-5.2—Mistral Medium 3.5 128B5.9%SourceNot directly comparable
Reasoning
ARC-AGI-2
GPT-5.252.9%SourceMistral Medium 3.5 128B—Not directly comparable
Knowledge
GPQA
GPT-5.292.4%SourceMistral Medium 3.5 128B—Not directly comparable
Math
Multimodal
Frequently asked questions
Which is better, GPT-5.2 or Mistral Medium 3.5 128B?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-5.2 or Mistral Medium 3.5 128B?
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, GPT-5.2 or Mistral Medium 3.5 128B?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, GPT-5.2 or Mistral Medium 3.5 128B?
For the stated presets, chat costs $0.00875 on GPT-5.2 and $0.00525 on Mistral Medium 3.5 128B; repository review costs $0.1295 and $0.0975; the cache-heavy agent loop costs $0.525 and $0.405. GPT-5.2 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-5.2 or Mistral Medium 3.5 128B?
GPT-5.2 has the larger documented context window: 400K, compared with 256K.
Related comparisons
Last updated August 13, 2026
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