Long documents
Prompts that approach the documented context limit
GPT-5.2-Codex
GPT-5.2-Codex has the larger documented context window.
Confidence: documented
GPT-5.2-Codex vs Mistral Medium 3.5 128B
Updated September 29, 2026. Rank says GPT-5.2-Codex is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
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Decision reading
GPT-5.2-Codex has the higher public score estimate, 49.86 versus 36.09, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 2
- GPT-5.2-Codex only
- 3
- Mistral Medium 3.5 128B only
- 5
- Like-for-like categories
- 0 / 8
Estimated: GPT-5.2-Codex and Mistral Medium 3.5 128BHow the comparison works
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.
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-ratesCache-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-Codex 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-ratesCoding 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: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-5.2-Codex is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
45.2GPT-5.2-Codex26.6Mistral Medium 3.5 128B
Directional only · BenchAlign v5.7
GPT-5.2-Codex scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 GPT-5.2-Codex only
2 Shared
5 Mistral Medium 3.5 128B only
4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 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- GPT-5.2-Codex
- 34.6
- Estimated · #68/117
- Mistral Medium 3.5 128B
- 19.7
- Supported · #100/117
- Basis
- BenchAlign v5.7 lane · 2 vs 3 public rows
- Reading
- Directional only
Coding
Directional only- GPT-5.2-Codex
- 45.2
- Supported · #52/143
- Mistral Medium 3.5 128B
- 26.6
- Estimated · #102/143
- Basis
- BenchAlign v5.7 lane · 3 vs 2 public rows
- Reading
- Directional only
Knowledge
Directional only- GPT-5.2-Codex
- 51.8
- Estimated · #60/169
- Mistral Medium 3.5 128B
- 33.6
- Supported · #121/169
- Basis
- BenchAlign v5.7 lane · 0 vs 2 public rows
- Reading
- Directional only
Instruction following
Directional only- GPT-5.2-Codex
- 92.4
- #3/124
- Mistral Medium 3.5 128B
- 82.6
- #48/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.2-Codex
- 78.9
- Unranked · 2 rankable rows
- Mistral Medium 3.5 128B
- 69.9
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.2-Codex
- 73.5
- Unranked · 1 rankable row
- Mistral Medium 3.5 128B
- 56.7
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.2-Codex
- Not ranked
- Mistral Medium 3.5 128B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.2-Codex
- Not ranked
- Mistral Medium 3.5 128B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.2-Codex | Mistral Medium 3.5 128B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 34.6Estimated · #68/117 | 19.7Supported · #100/117 | Directional onlyBenchAlign v5.7 lane · 2 vs 3 public rows | Directional only |
| Coding | 45.2Supported · #52/143 | 26.6Estimated · #102/143 | Directional onlyBenchAlign v5.7 lane · 3 vs 2 public rows | Directional only |
| Knowledge | 51.8Estimated · #60/169 | 33.6Supported · #121/169 | Directional onlyBenchAlign v5.7 lane · 0 vs 2 public rows | Directional only |
| Instruction following | 92.4#3/124 | 82.6#48/124 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 78.9Unranked · 2 rankable rows | 69.9Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 73.5Unranked · 1 rankable row | 56.7Unranked · 1 rankable row | 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 |
| Math | Not ranked | Not ranked | 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 v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
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-Codex
- $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-Codex
- $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-Codex
- $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-Codex 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.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.2-CodexMistral Medium 3.5 128B
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.2-Codex
400K
Mistral Medium 3.5 128B
256K
API model ID
GPT-5.2-Codex
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-Codex
Not published
Mistral Medium 3.5 128B
Not published
Documented inputs
GPT-5.2-Codex
Not sourced
Mistral Medium 3.5 128B
Not sourced
Documented outputs
GPT-5.2-Codex
Not sourced
Mistral Medium 3.5 128B
Not sourced
Provider availability
GPT-5.2-Codex
Not sourced
Mistral Medium 3.5 128B
Not sourced
Reasoning profile
GPT-5.2-Codex
Reasoning
Mistral Medium 3.5 128B
Reasoning
Weight access
GPT-5.2-Codex
Proprietary
Mistral Medium 3.5 128B
Open Weight
License
GPT-5.2-Codex
Proprietary
Mistral Medium 3.5 128B
Open Weight
Release date
GPT-5.2-Codex
2025-12-18
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
- GPT-5.2-Codex has the higher public score estimate, 49.86 versus 36.09, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.1295 vs $0.0975. Cache-heavy agent loop: $0.525 vs $0.405.
- Context tradeoff
- GPT-5.2-Codex has the larger documented window (400K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-5.2-Codex or Mistral Medium 3.5 128B?
GPT-5.2-Codex has the higher public score estimate, 49.86 versus 36.09, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, GPT-5.2-Codex or Mistral Medium 3.5 128B?
GPT-5.2-Codex scores higher for coding on the public lane, 45.2 to 26.6. 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, GPT-5.2-Codex or Mistral Medium 3.5 128B?
GPT-5.2-Codex scores higher for agentic tasks on the public lane, 34.6 to 19.7. GPT-5.2-Codex 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-5.2-Codex or Mistral Medium 3.5 128B?
For the stated presets, chat costs $0.00875 on GPT-5.2-Codex 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-Codex 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-Codex or Mistral Medium 3.5 128B?
GPT-5.2-Codex has the larger documented context window: 400K, compared with 256K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence10 rows
Agentic
Gert Labs
Shared sourceGPT-5.2-Codex51.79%Mistral Medium 3.5 128B39.10%GPT-5.2-Codex leads this result
JobBench
GPT-5.2-Codex26.0%SourceMistral Medium 3.5 128B—Not directly comparable
τ³-bench results
GPT-5.2-Codex—Mistral Medium 3.5 128B91.4%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.2-Codex—Mistral Medium 3.5 128B39.0%SourceNot directly comparable
Coding
Vibe Code Bench
GPT-5.2-Codex37.91%SourceMistral Medium 3.5 128B—Not directly comparable
LiveCodeBench (Vals)
GPT-5.2-Codex88.0%SourceMistral Medium 3.5 128B—Not directly comparable
SWE-bench (Vals)
GPT-5.2-Codex72.4%SourceMistral Medium 3.5 128B66.4%SourceGPT-5.2-Codex leads this result
SWE-bench Verified
GPT-5.2-Codex—Mistral Medium 3.5 128B77.6%SourceNot directly comparable
Knowledge
10 public results · 2 shared
Watch GPT-5.2-Codex vs Mistral Medium 3.5 128B
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Last updated September 29, 2026