Long documents
Prompts that approach the documented context limit
Mistral Small 3.1
Mistral Small 3.1 has the larger documented context window.
Confidence: documented
Celeris-1 vs Mistral Small 3.1
Updated October 14, 2026. We do not rank this pair: at least one has no public score. 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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 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
- Celeris-1 only
- 3
- Mistral Small 3.1 only
- 6
- Like-for-like categories
- 0 / 8
Estimated: Celeris-1. 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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Mistral Small 3.1 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 Small 3.1 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedChat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Celeris-1 does not fit this workload in one request. Mistral Small 3.1 has no comparable published API token rate.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Celeris-1 does not fit this workload in one request. Mistral Small 3.1 does not fit this workload in one request. Celeris-1 has no published cached-input rate, so cached tokens use its listed input rate. Mistral Small 3.1 has no comparable published API token rate.
Confidence: rate-fallbackRepository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Celeris-1 does not fit this workload in one request. Mistral Small 3.1 has no comparable published API token rate.
Confidence: listed-rates
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.
11.0Celeris-1—Mistral Small 3.1
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.
3 Celeris-1 only
1 Shared
6 Mistral Small 3.1 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.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
MMLU-ProKnowledge
Normalized gap 9.1Celeris-1:75.9%Mistral Small 3.1:66.8%
Bars run 0–100 on each benchmark’s normalized display scale
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.
Agentic
Not comparable- Celeris-1
- 7.1
- Estimated · #120/124
- Mistral Small 3.1
- Not ranked
- Basis
- BenchAlign v5.8 lane · 0 vs 0 public rows
- Reading
- Not comparable
Coding
Not comparable- Celeris-1
- 11.0
- Estimated · #141/149
- Mistral Small 3.1
- Not ranked
- Basis
- BenchAlign v5.8 lane · 0 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Celeris-1
- 52.0
- Unranked · 3 rankable rows
- Mistral Small 3.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Celeris-1
- Not ranked
- Mistral Small 3.1
- 18.8
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Celeris-1
- 28.5
- Supported · #157/181
- Mistral Small 3.1
- Not ranked
- Basis
- BenchAlign v5.8 lane · 1 vs 4 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Celeris-1
- Not ranked
- Mistral Small 3.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Celeris-1
- Not ranked
- Mistral Small 3.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Celeris-1
- Not ranked
- Mistral Small 3.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Celeris-1 | Mistral Small 3.1 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 7.1Estimated · #120/124 | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows | Not comparable |
| Coding | 11.0Estimated · #141/149 | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 1 public rows | Not comparable |
| Reasoning | 52.0Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 18.8Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Knowledge | 28.5Supported · #157/181 | Not ranked | Not comparableBenchAlign v5.8 lane · 1 vs 4 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 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 v5.8) 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
- Celeris-1
- $0.00055
- Does not fit in one request
- Mistral Small 3.1
- API rate not published
- Fits in one request
Celeris-1 does not fit this workload in one request. Mistral Small 3.1 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Celeris-1
- $0.0121
- Does not fit in one request
- Mistral Small 3.1
- API rate not published
- Fits in one request
Celeris-1 does not fit this workload in one request. Mistral Small 3.1 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Celeris-1
- $0.051
- Does not fit in one request
- Cached input priced at the published list-input rate
- Mistral Small 3.1
- API rate not published
- Does not fit in one request
- Cached-input rate unavailable
Celeris-1 does not fit this workload in one request. Mistral Small 3.1 does not fit this workload in one request. Celeris-1 has no published cached-input rate, so cached tokens use its listed input rate. Mistral Small 3.1 has no comparable published API token 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.
SpecificationCeleris-1Mistral Small 3.1
Context window
Maximum documented context; output-token limits may be lower.
Celeris-1
131,072 tokens
Celeris-1 model guideMistral Small 3.1
128K
API model ID
Celeris-1
celeris-1
Celeris-1 model guideMistral Small 3.1
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Celeris-1
Not published
Celeris API pricingMistral Small 3.1
No comparable hosted API rate
Documented inputs
Celeris-1
text, image
Celeris-1 model guideMistral Small 3.1
Not sourced
Documented outputs
Celeris-1
Mistral Small 3.1
Not sourced
Provider availability
Celeris-1
Generally Available · Celeris OpenAI-compatible API (United States)
Celeris availabilityMistral Small 3.1
Not sourced
Reasoning profile
Celeris-1
Non-Reasoning
Mistral Small 3.1
Non-Reasoning
Weight access
Celeris-1
Proprietary
Mistral Small 3.1
Open Weight
License
Celeris-1
Proprietary
Mistral Small 3.1
Open Weight
Release date
Celeris-1
2026-07-22
Mistral Small 3.1
2025-03-17
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Mistral Small 3.1 has the larger documented window (128K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Celeris-1 or Mistral Small 3.1?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Celeris-1 or Mistral Small 3.1?
Mistral Small 3.1 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Celeris-1 or Mistral Small 3.1?
Mistral Small 3.1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Celeris-1 or Mistral Small 3.1?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, Celeris-1 or Mistral Small 3.1?
Mistral Small 3.1 has the larger documented context window: 128K, compared with 131,072 tokens.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence10 rows
Coding
HumanEval
Celeris-1—Mistral Small 3.188.4%SourceNot directly comparable
Reasoning
DROP
Celeris-181.4%SourceMistral Small 3.1—Not directly comparable
Multimodal
Knowledge
Instruction following
IFEval
Celeris-180.8%SourceMistral Small 3.1—Not directly comparable
Math
GSM8K
Celeris-193.7%SourceMistral Small 3.1—Not directly comparable
10 public results · 1 shared
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Last updated October 14, 2026