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
Mistral Large 3 vs Pareto 26.10 Preview
Updated November 13, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Share or export
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.
- API price per 1M tokens
Mistral Large 3: $0.50 input / $1.50 output
Pareto 26.10 Preview: $2.50 input / $7.50 output
- Context window
Mistral Large 3: 256K
Pareto 26.10 Preview: Not sourced
- Released
Mistral Large 3: December 2, 2025
Pareto 26.10 Preview: October 1, 2026
- Shared results
- 0
- Mistral Large 3 only
- 4
- Pareto 26.10 Preview only
- 3
- Like-for-like categories
- 0 / 8
Estimated: Mistral Large 3. 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.
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
Show secondary and unsupported calls
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-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Pareto 26.10 Preview 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
Pareto 26.10 Preview is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
Not enough matched evidence
A complete context comparison is not sourced.
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.
15.7Mistral Large 3—Pareto 26.10 Preview
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
Or describe your own job
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.
We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
4 Mistral Large 3 only
3 Pareto 26.10 Preview 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, reasoning, multimodal, 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
Not comparable- Mistral Large 3
- 18.9
- Estimated · #121/135
- Pareto 26.10 Preview
- Not ranked
- Basis
- BenchAlign v5.8 lane · 0 vs 1 public rows
- Reading
- Not comparable
Coding
Not comparable- Mistral Large 3
- 15.7
- Supported · #143/162
- Pareto 26.10 Preview
- Not ranked
- Basis
- BenchAlign v5.8 lane · 2 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Mistral Large 3
- 25.5
- Estimated · #134/160
- Pareto 26.10 Preview
- Not ranked
- Basis
- BenchAlign v5.8 lane · 0 vs 0 public rows
- Reading
- Not comparable
Multimodal
Not comparable- Mistral Large 3
- Not ranked
- Pareto 26.10 Preview
- Not ranked
- Basis
- BenchAlign v5.8 lane · 0 vs 0 public rows
- Reading
- Not comparable
Knowledge
Not comparable- Mistral Large 3
- 32.3
- Supported · #142/180
- Pareto 26.10 Preview
- Not ranked
- Basis
- BenchAlign v5.8 lane · 2 vs 1 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Mistral Large 3
- Not ranked
- Pareto 26.10 Preview
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Mistral Large 3
- 41.5
- #122/157
- Pareto 26.10 Preview
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Mistral Large 3
- Not ranked
- Pareto 26.10 Preview
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Mistral Large 3 | Pareto 26.10 Preview | Basis | Reading |
|---|---|---|---|---|
| Agentic | 18.9Estimated · #121/135 | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 1 public rows | Not comparable |
| Coding | 15.7Supported · #143/162 | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 1 public rows | Not comparable |
| Reasoning | 25.5Estimated · #134/160 | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows | Not comparable |
| Knowledge | 32.3Supported · #142/180 | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 1 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 41.5#122/157 | 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, reasoning, multimodal, 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
- Mistral Large 3
- $0.00125
- Fits in one request
- Pareto 26.10 Preview
- $0.00625
- Fit state unavailable
Mistral Large 3 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Mistral Large 3
- $0.0295
- Fits in one request
- Pareto 26.10 Preview
- $0.1475
- Fit state unavailable
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
- Mistral Large 3
- $0.125
- Fits in one request
- Cached input priced at the published list-input rate
- Pareto 26.10 Preview
- $0.175
- Fit state unavailable
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.
Your workload
Enter a month of tokens to price it at both models’ listed API rates.
- Mistral Large 3
- $8.00
- per month
- Pareto 26.10 Preview
- $40.00
- per month
Standard listed rates, no batch or volume discounts.
Cached input falls back to the list input rate only where a cached rate is unpublished
Price against score, among all priced models
Overall score against output price per 1M tokens, one model per family. Pricing as of November 12, 2026.
Mistral Large 3
- 21 models score at least as high as Mistral Large 3 for the same price or less. The nearest is Gemini 3.1 Flash-Lite (score 46.6, also $1.50 per 1M output tokens).
- Pareto 26.10 Preview has no overall score yet, so it is not plotted.
Gray dots are the other priced models. The dark step line is the efficiency frontier: for a model under it, another model costs no more and scores at least as high.
See Mistral Large 3 on the full price vs performance chartSpecification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationMistral Large 3Pareto 26.10 Preview
Context window
Maximum documented context; output-token limits may be lower.
Mistral Large 3
256K
Pareto 26.10 Preview
Not sourced
Unbiased Pareto 26.10 Preview model cardAPI model ID
Mistral Large 3
Not sourced
Pareto 26.10 Preview
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Mistral Large 3
Not published
Pareto 26.10 Preview
$0.25 per 1M cached input tokens
Unbiased direct API pricingDocumented inputs
Mistral Large 3
Pareto 26.10 Preview
text, image
OpenRouter Pareto 26.10 Preview routeDocumented outputs
Mistral Large 3
Pareto 26.10 Preview
Provider availability
Mistral Large 3
Not sourced
Pareto 26.10 Preview
Preview · Unbiased Chat Completions API, Unbiased Messages API, OpenRouter
Unbiased API documentationReasoning profile
Mistral Large 3
Non-Reasoning
Pareto 26.10 Preview
Reasoning
Weight access
Mistral Large 3
Open Weight
Pareto 26.10 Preview
Proprietary
License
Mistral Large 3
Open Weight
Pareto 26.10 Preview
Proprietary
Release date
Mistral Large 3
2025-12-02
Pareto 26.10 Preview
2026-10-01
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: $0.0295 vs $0.1475. Cache-heavy agent loop: $0.125 vs $0.175.
- Context tradeoff
- A complete documented context comparison is not available.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Mistral Large 3 or Pareto 26.10 Preview?
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, Mistral Large 3 or Pareto 26.10 Preview?
Pareto 26.10 Preview is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Mistral Large 3 or Pareto 26.10 Preview?
Pareto 26.10 Preview is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Mistral Large 3 or Pareto 26.10 Preview?
For the stated presets, chat costs $0.00125 on Mistral Large 3 and $0.00625 on Pareto 26.10 Preview; repository review costs $0.0295 and $0.1475; the cache-heavy agent loop costs $0.125 and $0.175. Mistral Large 3 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Mistral Large 3 or Pareto 26.10 Preview?
A complete documented context-window comparison is not available.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Mistral Large 3
API / mo$1,500
Self-host / mo$27,962
Break-evenNot reached at 70% load
Pareto 26.10 Preview
API / mo$7,500
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence7 rows
Agentic
Terminal-Bench 4.0
Mistral Large 3—Pareto 26.10 Preview50.80%SourceNot directly comparable
Coding
Knowledge
7 public results · 0 shared
Watch Mistral Large 3 vs Pareto 26.10 Preview
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Last updated November 13, 2026