Long documents
Prompts that approach the documented context limit
GPT-5.6 Sol
GPT-5.6 Sol has the larger documented context window.
Confidence: documented
GPT-5.6 Sol vs Mistral Small 4
Updated September 24, 2026. Rank says GPT-5.6 Sol 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
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.
- Shared results
- 0
- GPT-5.6 Sol only
- 37
- Mistral Small 4 only
- 0
- Like-for-like categories
- 0 / 8
Supported: GPT-5.6 Sol · Estimated: Mistral Small 4How 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 Small 4
Mistral Small 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 Small 4
Mistral Small 4 has the lower estimated token cost for this stated workload. Mistral Small 4 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 Small 4
Mistral Small 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 Small 4 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
Mistral Small 4 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.
71.6GPT-5.6 Sol25.0Mistral Small 4
Directional only · BenchAlign v5.7
GPT-5.6 Sol 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.
37 GPT-5.6 Sol 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-5.6 Sol
- 69.6
- Supported · #7/105
- Mistral Small 4
- 17.4
- Estimated · #92/105
- Basis
- BenchAlign v5.7 lane · 9 vs 0 public rows
- Reading
- Directional only
Coding
Directional only- GPT-5.6 Sol
- 71.6
- Supported · #6/135
- Mistral Small 4
- 25.0
- Estimated · #99/135
- Basis
- BenchAlign v5.7 lane · 12 vs 0 public rows
- Reading
- Directional only
Knowledge
Directional only- GPT-5.6 Sol
- 78.8
- Supported · #7/158
- Mistral Small 4
- 34.7
- Estimated · #110/158
- Basis
- BenchAlign v5.7 lane · 8 vs 0 public rows
- Reading
- Directional only
Instruction following
Directional only- GPT-5.6 Sol
- 87.7
- #28/124
- Mistral Small 4
- 55.7
- #77/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.6 Sol
- 72.1
- #8/19
- Mistral Small 4
- 56.2
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.6 Sol
- 87.6
- #5/50
- Mistral Small 4
- 43.8
- Unranked · 1 rankable row
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.6 Sol
- Not ranked
- Mistral Small 4
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.6 Sol
- 96.8
- Unranked · 3 rankable rows
- Mistral Small 4
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.6 Sol | Mistral Small 4 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 69.6Supported · #7/105 | 17.4Estimated · #92/105 | Directional onlyBenchAlign v5.7 lane · 9 vs 0 public rows | Directional only |
| Coding | 71.6Supported · #6/135 | 25.0Estimated · #99/135 | Directional onlyBenchAlign v5.7 lane · 12 vs 0 public rows | Directional only |
| Knowledge | 78.8Supported · #7/158 | 34.7Estimated · #110/158 | Directional onlyBenchAlign v5.7 lane · 8 vs 0 public rows | Directional only |
| Instruction following | 87.7#28/124 | 55.7#77/124 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 72.1#8/19 | 56.2Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multimodal | 87.6#5/50 | 43.8Unranked · 1 rankable row | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 96.8Unranked · 3 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 v5.7) 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-5.6 Sol
- $0.014
- Fits in one request
- Mistral Small 4
- $0.00045
- Fits in one request
Mistral Small 4 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-5.6 Sol
- $0.26
- Fits in one request
- Mistral Small 4
- $0.0093
- Fits in one request
Mistral Small 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-5.6 Sol
- $0.36
- Fits in one request
- Mistral Small 4
- $0.039
- Fits in one request
- Cached input priced at the published list-input rate
Mistral Small 4 has the lower modeled cost
Mistral Small 4 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-5.6 SolMistral Small 4
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.6 Sol
1.05M
OpenAI model catalogMistral Small 4
256K
API model ID
GPT-5.6 Sol
gpt-5.6-sol
OpenAI model catalogMistral Small 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-5.6 Sol
$0.4 per 1M cached input tokens
OpenAI pricingMistral Small 4
Not published
Documented inputs
GPT-5.6 Sol
text, image
OpenAI model catalogMistral Small 4
Not sourced
Documented outputs
GPT-5.6 Sol
Mistral Small 4
Not sourced
Provider availability
GPT-5.6 Sol
Generally Available · OpenAI Responses API
OpenAI model catalogMistral Small 4
Not sourced
Reasoning profile
GPT-5.6 Sol
Reasoning
Mistral Small 4
Non-Reasoning
Weight access
GPT-5.6 Sol
Proprietary
Mistral Small 4
Open Weight
License
GPT-5.6 Sol
Proprietary
Mistral Small 4
Open Weight
Release date
GPT-5.6 Sol
2026-07-09
Mistral Small 4
2026-02-20
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: $0.26 vs $0.0093. Cache-heavy agent loop: $0.36 vs $0.039.
- Context tradeoff
- GPT-5.6 Sol has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-5.6 Sol or Mistral Small 4?
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-5.6 Sol or Mistral Small 4?
GPT-5.6 Sol scores higher for coding on the public lane, 71.6 to 25. Mistral Small 4 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-5.6 Sol or Mistral Small 4?
GPT-5.6 Sol scores higher for agentic tasks on the public lane, 69.6 to 17.4. Mistral Small 4 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-5.6 Sol or Mistral Small 4?
For the stated presets, chat costs $0.014 on GPT-5.6 Sol and $0.00045 on Mistral Small 4; repository review costs $0.26 and $0.0093; the cache-heavy agent loop costs $0.36 and $0.039. Mistral Small 4 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-5.6 Sol or Mistral Small 4?
GPT-5.6 Sol has the larger documented context window: 1.05M, compared with 256K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
GPT-5.6 Sol
API / mo$18,000
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Mistral Small 4
API / mo$563
Self-host / mo$2,278
Break-even266M/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 evidence37 rows
Agentic
Terminal-Bench 3.0
GPT-5.6 Sol34.6%SourceMistral Small 4—Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Sol91.9%SourceMistral Small 4—Not directly comparable
BrowseComp
GPT-5.6 Sol92.2%SourceMistral Small 4—Not directly comparable
OSWorld 2.0
GPT-5.6 Sol62.6%SourceMistral Small 4—Not directly comparable
CyberGym
GPT-5.6 Sol84.5%SourceMistral Small 4—Not directly comparable
ExploitGym
GPT-5.6 Sol33.7%SourceMistral Small 4—Not directly comparable
Toolathlon
GPT-5.6 Sol58%SourceMistral Small 4—Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.6 Sol85.8%SourceMistral Small 4—Not directly comparable
ApprenticeBench
GPT-5.6 Sol26%SourceMistral Small 4—Not directly comparable
Coding
Bug Hunt Bench
GPT-5.6 Sol42 fixesSourceMistral Small 4—Not directly comparable
SWE-bench Pro
GPT-5.6 Sol64.6%SourceMistral Small 4—Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Sol91.9%SourceMistral Small 4—Not directly comparable
DeepSWE
GPT-5.6 Sol72.7%SourceMistral Small 4—Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Sol60.6%SourceMistral Small 4—Not directly comparable
FrontierSWE v2
GPT-5.6 Sol32.2%SourceMistral Small 4—Not directly comparable
cursorBench32
GPT-5.6 Sol67.2%SourceMistral Small 4—Not directly comparable
VulcanBench v3
GPT-5.6 Sol87.0%SourceMistral Small 4—Not directly comparable
VulcanBench CII v1
GPT-5.6 Sol86.5%SourceMistral Small 4—Not directly comparable
LiveCodeBench (Vals)
GPT-5.6 Sol82.6%SourceMistral Small 4—Not directly comparable
SWE-bench (Vals)
GPT-5.6 Sol96.2%SourceMistral Small 4—Not directly comparable
cursorBench40
GPT-5.6 Sol41.7%SourceMistral Small 4—Not directly comparable
Reasoning
Multimodal
Knowledge
GPQA
GPT-5.6 Sol94.6%SourceMistral Small 4—Not directly comparable
GPQA-D
GPT-5.6 Sol94.6%SourceMistral Small 4—Not directly comparable
HLE-Verified
GPT-5.6 Sol54.5%SourceMistral Small 4—Not directly comparable
LABBench2
GPT-5.6 Sol82.1%SourceMistral Small 4—Not directly comparable
HealthBench Professional
GPT-5.6 Sol60.5%SourceMistral Small 4—Not directly comparable
HealthBench Hard
GPT-5.6 Sol33.1%SourceMistral Small 4—Not directly comparable
GPQA Diamond (Vals)
GPT-5.6 Sol95.2%SourceMistral Small 4—Not directly comparable
MMLU-Pro (Vals)
GPT-5.6 Sol89.1%SourceMistral Small 4—Not directly comparable
Math
37 public results · 0 shared
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Last updated September 24, 2026