Coding
Like-for-like- GPT-5.5
- 67.7
- Supported · #8/183
- Mistral Large 3
- 26.0
- Supported · #176/183
- Basis
- BenchAlign lane · 9 vs 0 public rows
- Reading
- GPT-5.5 leads
GPT-5.5 vs Mistral Large 3
Updated September 4, 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.
Coding work
Code generation, repair, and software-engineering tasks
GPT-5.5
GPT-5.5 leads on the public coding lane, 67.7 to 26, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Long documents
Prompts that approach the documented context limit
GPT-5.5
GPT-5.5 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Mistral Large 3
Mistral Large 3 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
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Mistral Large 3
Mistral Large 3 has the lower estimated token cost for this stated workload. Mistral Large 3 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
Mistral Large 3
Mistral Large 3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Mistral Large 3 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.
38 GPT-5.5 only
- Shared results
- 0
- GPT-5.5 only
- 38
- Mistral Large 3 only
- 0
- Like-for-like categories
- 1 / 8
3 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.
Agentic
Directional only- GPT-5.5
- 63.9
- Supported · #15/151
- Mistral Large 3
- 42.8
- Estimated · #111/151
- Basis
- BenchAlign lane · 13 vs 0 public rows
- Reading
- Directional only
Knowledge
Directional only- GPT-5.5
- 73.3
- Supported · #7/181
- Mistral Large 3
- 43.8
- Estimated · #124/181
- Basis
- BenchAlign lane · 6 vs 0 public rows
- Reading
- Directional only
Instruction following
Directional only- GPT-5.5
- 92.9
- #7/120
- Mistral Large 3
- 41.4
- #100/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.5
- 63.5
- #15/22
- Mistral Large 3
- 46.0
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.5
- 69.6
- Unranked · 3 rankable rows
- Mistral Large 3
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.5
- Not ranked
- Mistral Large 3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.5
- 71.3
- #19/48
- Mistral Large 3
- 42.1
- Unranked · 1 rankable row
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.5 | Mistral Large 3 | Basis | Reading |
|---|---|---|---|---|
| Coding | 67.7Supported · #8/183 | 26.0Supported · #176/183 | Like-for-likeBenchAlign lane · 9 vs 0 public rows | GPT-5.5 leads |
| Agentic | 63.9Supported · #15/151 | 42.8Estimated · #111/151 | Directional onlyBenchAlign lane · 13 vs 0 public rows | Directional only |
| Knowledge | 73.3Supported · #7/181 | 43.8Estimated · #124/181 | Directional onlyBenchAlign lane · 6 vs 0 public rows | Directional only |
| Instruction following | 92.9#7/120 | 41.4#100/120 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 63.5#15/22 | 46.0Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Math | 69.6Unranked · 3 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 | 71.3#19/48 | 42.1Unranked · 1 rankable row | 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) 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-5.5
- $0.02
- Fits in one request
- Mistral Large 3
- $0.00125
- Fits in one request
Mistral Large 3 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-5.5
- $0.34
- Fits in one request
- Mistral Large 3
- $0.0295
- Fits in one request
Mistral Large 3 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-5.5
- $0.5
- Fits in one request
- Mistral Large 3
- $0.125
- Fits in one request
- Cached input priced at the published list-input rate
Mistral Large 3 has the lower modeled cost
Mistral Large 3 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-5.5Mistral Large 3
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.5
Mistral Large 3
256K
API model ID
GPT-5.5
gpt-5.5
OpenAI pricingMistral Large 3
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.5
$0.5 per 1M cached input tokens
OpenAI pricingMistral Large 3
Not published
Documented inputs
GPT-5.5
Not sourced
Mistral Large 3
Not sourced
Documented outputs
GPT-5.5
Not sourced
Mistral Large 3
Not sourced
Provider availability
GPT-5.5
Not sourced
Mistral Large 3
Not sourced
Reasoning profile
GPT-5.5
Reasoning
Mistral Large 3
Non-Reasoning
Weight access
GPT-5.5
Proprietary
Mistral Large 3
Proprietary
License
GPT-5.5
Proprietary
Mistral Large 3
Proprietary
Release date
GPT-5.5
2026-04-23
Mistral Large 3
2025-12-02
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: $0.34 vs $0.0295. Cache-heavy agent loop: $0.5 vs $0.125.
- Context tradeoff
- GPT-5.5 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
GPT-5.5
API / mo$26,250
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Mistral Large 3
API / mo$1,500
Self-host / mo$9,110
Break-even380M/day
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence38 rows
Agentic
Terminal-Bench 2.0
GPT-5.582%SourceMistral Large 3—Not directly comparable
CyberGym
GPT-5.581.8%SourceMistral Large 3—Not directly comparable
BrowseComp
GPT-5.584.4%SourceMistral Large 3—Not directly comparable
OSWorld-Verified
GPT-5.578.7%SourceMistral Large 3—Not directly comparable
MCP Atlas
GPT-5.575.3%SourceMistral Large 3—Not directly comparable
Toolathlon
GPT-5.555.6%SourceMistral Large 3—Not directly comparable
τ²-bench results
GPT-5.598%SourceMistral Large 3—Not directly comparable
Gert Labs
GPT-5.572.93%SourceMistral Large 3—Not directly comparable
ResearchClawBench
GPT-5.517.0%SourceMistral Large 3—Not directly comparable
OSWorld 2.0
GPT-5.513.0%SourceMistral Large 3—Not directly comparable
JobBench
GPT-5.542.7%SourceMistral Large 3—Not directly comparable
ExploitGym
GPT-5.513.4%SourceMistral Large 3—Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.576.4%SourceMistral Large 3—Not directly comparable
Coding
SWE-bench Pro
GPT-5.558.6%SourceMistral Large 3—Not directly comparable
Terminal-Bench 2.0
GPT-5.582.0%SourceMistral Large 3—Not directly comparable
Vibe Code Bench
GPT-5.569.85%SourceMistral Large 3—Not directly comparable
React Native Evals
GPT-5.584.7%SourceMistral Large 3—Not directly comparable
cursorBench31
GPT-5.559.2%SourceMistral Large 3—Not directly comparable
cursorBench32
GPT-5.558.4%SourceMistral Large 3—Not directly comparable
FrontierCode 1.1 Main
GPT-5.543.0%SourceMistral Large 3—Not directly comparable
LiveCodeBench (Vals)
GPT-5.585.3%SourceMistral Large 3—Not directly comparable
SWE-bench (Vals)
GPT-5.582.6%SourceMistral Large 3—Not directly comparable
Reasoning
Knowledge
GPQA
GPT-5.593.6%SourceMistral Large 3—Not directly comparable
GPQA-D
GPT-5.593.6%SourceMistral Large 3—Not directly comparable
HLE
GPT-5.552.2%SourceMistral Large 3—Not directly comparable
HLE w/o tools
GPT-5.541.4%SourceMistral Large 3—Not directly comparable
GPQA Diamond (Vals)
GPT-5.593.2%SourceMistral Large 3—Not directly comparable
MMLU-Pro (Vals)
GPT-5.588.1%SourceMistral Large 3—Not directly comparable
Math
Multimodal
Frequently asked questions
Which is better, GPT-5.5 or Mistral Large 3?
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-5.5 or Mistral Large 3?
GPT-5.5 leads the public coding lane, 67.7 to 26, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, GPT-5.5 or Mistral Large 3?
GPT-5.5 scores higher for agentic tasks on the public lane, 63.9 to 42.8. Mistral Large 3 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-5.5 or Mistral Large 3?
For the stated presets, chat costs $0.02 on GPT-5.5 and $0.00125 on Mistral Large 3; repository review costs $0.34 and $0.0295; the cache-heavy agent loop costs $0.5 and $0.125. Mistral Large 3 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-5.5 or Mistral Large 3?
GPT-5.5 has the larger documented context window: 1M, compared with 256K.
Related comparisons
Last updated September 4, 2026
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