Long documents
Prompts that approach the documented context limit
Mistral Large 4
Mistral Large 4 has the larger documented context window.
Confidence: documented
GPT-5.2 vs Mistral Large 4
Updated October 6, 2026. Rank says GPT-5.2 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
Share or export
Decision reading
GPT-5.2 has the higher public point estimate, 60.31 versus 53.69. Their conditional score ranges overlap. These ranges do not establish rank confidence. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 1
- GPT-5.2 only
- 15
- Mistral Large 4 only
- 2
- Like-for-like categories
- 1 / 8
Supported: GPT-5.2 · Estimated: Mistral Large 4. Conditional ranges do not establish rank confidence.How 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 Large 4
Mistral Large 4 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 Large 4
Mistral Large 4 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.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Mistral Large 4
Mistral Large 4 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 Large 4 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Mistral Large 4 is not ranked on the public lane for agentic, so no winner is named for agentic.
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.
38.3GPT-5.2—Mistral Large 4
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
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.
15 GPT-5.2 only
1 Shared
2 Mistral Large 4 only
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 v5.8 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- GPT-5.2
- 57.8
- Supported · #48/173
- Mistral Large 4
- 55.1
- Supported · #51/173
- Basis
- BenchAlign v5.8 lane · 1 vs 0 public rows
- Reading
- GPT-5.2 leads · intervals overlap
Agentic
Not comparable- GPT-5.2
- 43.1
- Supported · #55/120
- Mistral Large 4
- Not ranked
- Basis
- BenchAlign v5.8 lane · 4 vs 2 public rows
- Reading
- Not comparable
Coding
Not comparable- GPT-5.2
- 38.3
- Supported · #66/144
- Mistral Large 4
- Not ranked
- Basis
- BenchAlign v5.8 lane · 3 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.2
- 60.7
- Unranked · 4 rankable rows
- Mistral Large 4
- 78.2
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.2
- 67.3
- #22/49
- Mistral Large 4
- 73.6
- Unranked · 1 rankable row
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.2
- Not ranked
- Mistral Large 4
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.2
- 91.2
- #15/125
- Mistral Large 4
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.2
- 57.4
- Unranked · 2 rankable rows
- Mistral Large 4
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.2 | Mistral Large 4 | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 57.8Supported · #48/173 | 55.1Supported · #51/173 | Like-for-likeBenchAlign v5.8 lane · 1 vs 0 public rows | GPT-5.2 leads · intervals overlap |
| Agentic | 43.1Supported · #55/120 | Not ranked | Not comparableBenchAlign v5.8 lane · 4 vs 2 public rows | Not comparable |
| Coding | 38.3Supported · #66/144 | Not ranked | Not comparableBenchAlign v5.8 lane · 3 vs 1 public rows | Not comparable |
| Reasoning | 60.7Unranked · 4 rankable rows | 78.2Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | 67.3#22/49 | 73.6Unranked · 1 rankable row | 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 |
| Instruction following | 91.2#15/125 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 57.4Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) 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
- $0.00875
- Fits in one request
- Mistral Large 4
- $0.00172
- Fits in one request
Mistral Large 4 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 Large 4
- $0.04027
- Fits in one request
Mistral Large 4 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 Large 4
- $0.0485
- Fits in one request
Mistral Large 4 has the lower modeled cost
GPT-5.2 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-5.2Mistral Large 4
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.2
400K
Mistral Large 4
API model ID
GPT-5.2
Not sourced
Mistral Large 4
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 Large 4
$0.07 per 1M cached input tokens
Mistral AI Mistral Large 4 model documentationDocumented inputs
GPT-5.2
Not sourced
Mistral Large 4
Not sourced
Documented outputs
GPT-5.2
Not sourced
Mistral Large 4
Not sourced
Provider availability
GPT-5.2
Not sourced
Mistral Large 4
Not sourced
Reasoning profile
GPT-5.2
Reasoning
Mistral Large 4
Hybrid
Weight access
GPT-5.2
Proprietary
Mistral Large 4
Pending
License
GPT-5.2
Proprietary
Mistral Large 4
Pending
Release date
GPT-5.2
2025-12-11
Mistral Large 4
2026-10-06
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 has the higher public point estimate, 60.31 versus 53.69. Their conditional score ranges overlap. These ranges do not establish rank confidence.
- Workload cost
- Repository review: $0.1295 vs $0.04027. Cache-heavy agent loop: $0.525 vs $0.0485.
- Context tradeoff
- Mistral Large 4 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-5.2 or Mistral Large 4?
GPT-5.2 has the higher public point estimate, 60.31 versus 53.69. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-5.2 or Mistral Large 4?
Mistral Large 4 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT-5.2 or Mistral Large 4?
Mistral Large 4 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-5.2 or Mistral Large 4?
For the stated presets, chat costs $0.00875 on GPT-5.2 and $0.00172 on Mistral Large 4; repository review costs $0.1295 and $0.04027; the cache-heavy agent loop costs $0.525 and $0.0485. GPT-5.2 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 Large 4?
Mistral Large 4 has the larger documented context window: 1M, compared with 400K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence18 rows
Agentic
BrowseComp
GPT-5.265.8%SourceMistral Large 4—Not directly comparable
OSWorld-Verified
GPT-5.247.3%SourceMistral Large 4—Not directly comparable
Gert Labs
GPT-5.246.54%SourceMistral Large 4—Not directly comparable
JobBench
GPT-5.234.3%SourceMistral Large 4—Not directly comparable
Cybench
GPT-5.2—Mistral Large 493.0%SourceNot directly comparable
Finance Agent v2
GPT-5.2—Mistral Large 454.7%SourceNot directly comparable
Coding
SWE-bench Verified
GPT-5.280%SourceMistral Large 4—Not directly comparable
SWE-bench Pro
GPT-5.255.6%SourceMistral Large 4—Not directly comparable
Vibe Code Bench
Shared sourceGPT-5.253.50%Mistral Large 478.40%Mistral Large 4 leads this result
Reasoning
Multimodal
Knowledge
GPQA
GPT-5.292.4%SourceMistral Large 4—Not directly comparable
Math
18 public results · 1 shared
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Last updated October 6, 2026