Ember-1 vs Mistral Small 3
Mistral Small 3 costs 187.5x less per output token than Ember-1 ($0.08 vs $15.00 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- 52.6
- Overall
- No data
- Output $/M
- $15.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.08
Ember-1 or Mistral Small 3: which to pick
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; Mistral Small 3 has none, so the two cannot be ranked on coding.
Pick Mistral Small 3 for output tokens 187.5x cheaper ($0.08 vs $15.00 per million).
No benchmark has tested both yet. Ember-1 has results on Terminal-Bench 4 (Vals AI), Vibe Code Bench (Vals AI) and Code migration (Vals AI); Mistral Small 3 on LiveCodeBench (Artificial Analysis run), LMArena Text: coding and LMArena Text: software and IT. Compare them on price and context instead.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only Ember-1 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Mistral Small 3 has a AA Intelligence Index result, 6.7.
Directional only
Long documents
Listed context window
Ember-1
Ember-1 takes 1.05M tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Small 3
Ember-1 $0.011, Mistral Small 3 $0.0001 per turn.
List prices
At a glance
- Coding index
- Ember-1 52.6 from 2 benchmarks, Mistral Small 3 not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Ember-1 $15.00 against Mistral Small 3 $0.08 per million output tokens, 187.5x apart.
- Context window
- Ember-1 takes 1.05M tokens, Mistral Small 3 33K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index8 boards measured
| Benchmark | Ember-1 | Mistral Small 3 | Leader |
|---|---|---|---|
| Terminal-Bench 4 (Vals AI) | 20.7% | Not measured | Only Ember-1 measured |
| Vibe Code Bench (Vals AI) | 83.9% | Not measured | Only Ember-1 measured |
| Code migration (Vals AI) | 32.5% | Not measured | Only Ember-1 measured |
| IOI (Vals AI) | 46.3% | Not measured | Only Ember-1 measured |
| CyberBench (Vals AI) | 69.5% | Not measured | Only Ember-1 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 25.2% | Only Mistral Small 3 measured |
| LMArena Text: coding | Not measured | 1246 | Only Mistral Small 3 measured |
| LMArena Text: software and IT | Not measured | 1248 | Only Mistral Small 3 measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Ember-1 | Mistral Small 3 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 6.7 | Only Mistral Small 3 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | Ember-1 | Mistral Small 3 | Leader |
|---|---|---|---|
| Finance Agent (Vals AI) | 51.8% | Not measured | Only Ember-1 measured |
| Tax Agent Bench (Vals AI) | 31.1% | Not measured | Only Ember-1 measured |
| Legal Research Bench (Vals AI) | 44.7% | Not measured | Only Ember-1 measured |
| Excel Modeling Benchmark (Vals AI) | 63.4% | Not measured | Only Ember-1 measured |
| BioMysteryBench (Vals AI) | 67.0% | Not measured | Only Ember-1 measured |
| Terminal-Bench-Science (Vals AI) | 5.7% | Not measured | Only Ember-1 measured |
| Vals Science Index | 31.8% | Not measured | Only Ember-1 measured |
| τ²-Bench Telecom | Not measured | 19.6% | Only Mistral Small 3 measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Ember-1 | Mistral Small 3 | Leader |
|---|---|---|---|
| MysteryMechanism (Vals AI) | 22.5% | Not measured | Only Ember-1 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 46.2% | Only Mistral Small 3 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 3.8% | Only Mistral Small 3 measured |
| LMArena Text: hard prompts | Not measured | 1233 | Only Mistral Small 3 measured |
| LMArena Text: expert | Not measured | 1202 | Only Mistral Small 3 measured |
| LMArena Text: hard prompts in English | Not measured | 1243 | Only Mistral Small 3 measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Ember-1 | Mistral Small 3 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 4.3 | Only Mistral Small 3 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 4.3% | Only Mistral Small 3 measured |
| LMArena Text: math | Not measured | 1240 | Only Mistral Small 3 measured |
| LMArena Text: mathematical work | Not measured | 1243 | Only Mistral Small 3 measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | Ember-1 | Mistral Small 3 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 65.2% | Only Mistral Small 3 measured |
| LMArena Text: science | Not measured | 1235 | Only Mistral Small 3 measured |
| LMArena Text: medicine and healthcare | Not measured | 1215 | Only Mistral Small 3 measured |
| LMArena Text: legal and government | Not measured | 1219 | Only Mistral Small 3 measured |
| LMArena Text: business and finance | Not measured | 1205 | Only Mistral Small 3 measured |
| Neither model has a result on the other 13 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index18 boards measured
| Benchmark | Ember-1 | Mistral Small 3 | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 66.2% | Not measured | Only Ember-1 measured |
| IFBench | Not measured | 26.4% | Only Mistral Small 3 measured |
| Long context reasoning (AA-LCR) | Not measured | 0.0% | Only Mistral Small 3 measured |
| LMArena Text | Not measured | 1234 | Only Mistral Small 3 measured |
| LMArena Text: instruction following | Not measured | 1214 | Only Mistral Small 3 measured |
| LMArena Text: creative writing | Not measured | 1194 | Only Mistral Small 3 measured |
| LMArena Text: English | Not measured | 1256 | Only Mistral Small 3 measured |
| LMArena Text: non-English | Not measured | 1198 | Only Mistral Small 3 measured |
| LMArena Text: Chinese | Not measured | 1204 | Only Mistral Small 3 measured |
| LMArena Text: German | Not measured | 1211 | Only Mistral Small 3 measured |
| LMArena Text: Japanese | Not measured | 1111 | Only Mistral Small 3 measured |
| LMArena Text: Korean | Not measured | 1188 | Only Mistral Small 3 measured |
| LMArena Text: French | Not measured | 1204 | Only Mistral Small 3 measured |
| LMArena Text: Russian | Not measured | 1216 | Only Mistral Small 3 measured |
| LMArena Text: multi-turn | Not measured | 1217 | Only Mistral Small 3 measured |
| LMArena Text: longer queries | Not measured | 1246 | Only Mistral Small 3 measured |
| LMArena Text: writing and language | Not measured | 1214 | Only Mistral Small 3 measured |
| LMArena Text: entertainment and media | Not measured | 1184 | Only Mistral Small 3 measured |
| Neither model has a result on the other 28 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Mistral Small 3 costs less
Repository review
50K input + 3K output tokens
Mistral Small 3 costs less
Agent loop
200K input + 10K output tokens
Mistral Small 3 costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Fireworks Ember-1 | Mistral Small 3 |
|---|---|---|
| Maker | Fireworks | Mistral AI |
| Weights | No public weights listed | Open weights |
| Released | September 24, 2026 | January 30, 2025 |
| Context window | 1.05M | 33K |
| Input $/Mtok | $3.00 | $0.05 |
| Output $/Mtok | $15.00 | $0.08 |
| Coding index | 52.6 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Ember-1 better than Mistral Small 3 for coding?
Only Ember-1 has a coding score, 52.6 on the Vibe Coding Index; Mistral Small 3 has none, so the two cannot be ranked on coding. Pick Mistral Small 3 for output tokens 187.5x cheaper ($0.08 vs $15.00 per million).
Which is cheaper, Ember-1 or Mistral Small 3?
Ember-1 costs $3.00 per million input tokens and $15.00 per million output tokens; Mistral Small 3 costs $0.05 and $0.08. Cost per solved task is known for neither of them.
Which has the bigger context window, Ember-1 or Mistral Small 3?
Ember-1, with 1.05M tokens against 33K.
Has any benchmark tested both Ember-1 and Mistral Small 3?
No benchmark has tested both yet. Ember-1 has results on Terminal-Bench 4 (Vals AI), Vibe Code Bench (Vals AI) and Code migration (Vals AI); Mistral Small 3 on LiveCodeBench (Artificial Analysis run), LMArena Text: coding and LMArena Text: software and IT. Compare them on price and context instead. Data as of October 21, 2026.