Chat turn cost
1K fresh input + 500 output tokens
Step 3.5 Flash
Step 3.5 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Mistral Large 3 vs Step 3.5 Flash
Updated September 29, 2026. Rank says Step 3.5 Flash 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
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
- Mistral Large 3 only
- 0
- Step 3.5 Flash only
- 0
- Like-for-like categories
- 0 / 8
Estimated: Mistral Large 3 and Step 3.5 FlashHow 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
Step 3.5 Flash
Step 3.5 Flash 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. Step 3.5 Flash 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
Step 3.5 Flash
Step 3.5 Flash 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
Step 3.5 Flash 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
Step 3.5 Flash 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
No clear pick
The documented context windows are equal.
Confidence: documented
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.
18.4Mistral Large 3—Step 3.5 Flash
Not comparable · BenchAlign v5.7
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.
Evidence parity totals are not available.
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
Not comparable- Mistral Large 3
- 17.1
- Estimated · #105/117
- Step 3.5 Flash
- Not ranked
- Basis
- BenchAlign v5.7 lane · 0 vs 0 public rows
- Reading
- Not comparable
Coding
Not comparable- Mistral Large 3
- 18.4
- Supported · #132/143
- Step 3.5 Flash
- Not ranked
- Basis
- BenchAlign v5.7 lane · 0 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Mistral Large 3
- 46.9
- Unranked · 2 rankable rows
- Step 3.5 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Mistral Large 3
- 43.2
- Unranked · 1 rankable row
- Step 3.5 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Mistral Large 3
- 33.6
- Estimated · #122/169
- Step 3.5 Flash
- Not ranked
- Basis
- BenchAlign v5.7 lane · 0 vs 0 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Mistral Large 3
- Not ranked
- Step 3.5 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Mistral Large 3
- 40.0
- #101/124
- Step 3.5 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Mistral Large 3
- Not ranked
- Step 3.5 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Mistral Large 3 | Step 3.5 Flash | Basis | Reading |
|---|---|---|---|---|
| Agentic | 17.1Estimated · #105/117 | Not ranked | Not comparableBenchAlign v5.7 lane · 0 vs 0 public rows | Not comparable |
| Coding | 18.4Supported · #132/143 | Not ranked | Not comparableBenchAlign v5.7 lane · 0 vs 0 public rows | Not comparable |
| Reasoning | 46.9Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 43.2Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 33.6Estimated · #122/169 | Not ranked | Not comparableBenchAlign v5.7 lane · 0 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 40.0#101/124 | 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
- Mistral Large 3
- $0.00125
- Fits in one request
- Step 3.5 Flash
- $0.00025
- Fits in one request
Step 3.5 Flash 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
- Step 3.5 Flash
- $0.0059
- Fits in one request
Step 3.5 Flash 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
- Step 3.5 Flash
- $0.025
- Fits in one request
- Cached input priced at the published list-input rate
Step 3.5 Flash has the lower modeled cost
Mistral Large 3 has no published cached-input rate, so cached tokens use its listed input rate. Step 3.5 Flash 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.
SpecificationMistral Large 3Step 3.5 Flash
Context window
Maximum documented context; output-token limits may be lower.
Mistral Large 3
256K
Step 3.5 Flash
256K
API model ID
Mistral Large 3
Not sourced
Step 3.5 Flash
Not sourced
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
Step 3.5 Flash
Not published
Documented inputs
Mistral Large 3
Not sourced
Step 3.5 Flash
Not sourced
Documented outputs
Mistral Large 3
Not sourced
Step 3.5 Flash
Not sourced
Provider availability
Mistral Large 3
Not sourced
Step 3.5 Flash
Not sourced
Reasoning profile
Mistral Large 3
Non-Reasoning
Step 3.5 Flash
Non-Reasoning
Weight access
Mistral Large 3
Proprietary
Step 3.5 Flash
Open Weight
License
Mistral Large 3
Proprietary
Step 3.5 Flash
Open Weight
Release date
Mistral Large 3
2025-12-02
Step 3.5 Flash
2026-01-20
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: $0.0295 vs $0.0059. Cache-heavy agent loop: $0.125 vs $0.025.
- Context tradeoff
- Both models list 256K.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Mistral Large 3 or Step 3.5 Flash?
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 Step 3.5 Flash?
Step 3.5 Flash 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 Step 3.5 Flash?
Step 3.5 Flash 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 Step 3.5 Flash?
For the stated presets, chat costs $0.00125 on Mistral Large 3 and $0.00025 on Step 3.5 Flash; repository review costs $0.0295 and $0.0059; the cache-heavy agent loop costs $0.125 and $0.025. Mistral Large 3 has no published cached-input rate, so cached tokens use its listed input rate. Step 3.5 Flash has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Mistral Large 3 or Step 3.5 Flash?
Both models list the same context window, 256K.
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$9,110
Break-even380M/day
Step 3.5 Flash
API / mo$300
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
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Last updated September 29, 2026