vs
2:1
overall capability wins (0 ties across 3 scored). Ministral 3 14B leads overall.
- Pricing1:2
- Speed2:0
- Specs2:1
Verdict
Ministral 3 14B offers ~8.0x larger context (256K vs 32K); Ministral 3 14B allows ~4.0x more max output (33K vs 8K); Voxtral Small is ~1.3x cheaper on a blended token basis than Ministral 3 14B.
- Ministral 3 14B offers ~8.0x larger context (256K vs 32K)
- Ministral 3 14B allows ~4.0x more max output (33K vs 8K)
- Voxtral Small is ~1.3x cheaper on a blended token basis than Ministral 3 14B
Cost for 1M in + 250K out
Illustrative chat workload at primary-provider list prices.
- Ministral 3 14B
- $0.25
- Voxtral Small
- $0.17
Voxtral Small is about 1.4x cheaper on this workload.
Price per 1M tokens
Full comparison
Organization
Mistral
Mistral
Family
Mistral
Mistral
License
Apache 2.0
Apache 2.0
Open weights
Yes
Yes
Release
Dec 1, 2025
Jul 15, 2025
Knowledge cutoff
-
-
API / provider
Mistral
Mistral
Modalities
text, image → text
text, audio → text
Specs
Context window
256K
32K
Max output
33K
8K
Parameters
14B
24B
Pricing
Input $/1M
$0.20
$0.10
Output $/1M
$0.20
$0.30
Blended $/1M (3∶1)
$0.20
$0.15
Speed
tok/s
130
90
TTFT (s)
0.2
0.35
Reasoning
MMLU-Pro
69.3
—
GPQA Diamond
57.2
—
Humanity's Last Exam
4.6
—
AIME 2025
30.0
—
MATH-500
—
—
Humanity's Last Exam (with tools)
—
—
AA-Omniscience Accuracy
13.6
—
AA-LCR v1.1
26.3
—
CritPt
—
—
MMMU-Pro
49.8
—
IFBench
32.0
—
Chartography
—
—
Chartography (With Tools)
—
—
Coding
SWE-bench Verified
—
—
SWE-bench Pro
—
—
SWE-bench Multilingual
—
—
LiveCodeBench
35.1
—
Terminal-Bench 2.1
9.7
—
Aider Polyglot
—
—
Terminal-Bench 3
—
—
BigCodeBench
—
—
SciCode
23.8
—
CursorBench
—
—
SWE-Rebench
—
—
NL2Repo-Bench
—
—
DeepSWE
—
—
WebDev Arena
—
—
Terminal-Bench 4.0
—
—
CursorBench 4.0
—
—
FrontierCode v1.1 (Main)
—
—
Arena
LMArena Elo
—
—
| Metric | Ministral 3 14B | Voxtral Small | Delta |
|---|---|---|---|
| Identity | |||
| Organization | Mistral | Mistral | — |
| Family | Mistral | Mistral | — |
| License | Apache 2.0 | Apache 2.0 | — |
| Open weights | Yes | Yes | — |
| Release | Dec 1, 2025 | Jul 15, 2025 | — |
| Knowledge cutoff | - | - | — |
| API / provider | Mistral | Mistral | — |
| Modalities | text, image → text | text, audio → text | — |
| Specs | |||
| Context window | 256K | 32K | A +224K |
| Max output | 33K | 8K | A +25K |
| Parameters | 14B | 24B | B +10 |
| Pricing | |||
| Input $/1M | $0.20 | $0.10 | B +$0.10 |
| Output $/1M | $0.20 | $0.30 | A +$0.10 |
| Blended $/1M (3∶1) | $0.20 | $0.15 | B +$0.05 |
| Speed | |||
| tok/s | 130 | 90 | A +40 |
| TTFT (s) | 0.2 | 0.35 | A +0.15 |
| Reasoning | |||
| MMLU-Pro | 69.3 | — | — |
| GPQA Diamond | 57.2 | — | — |
| Humanity's Last Exam | 4.6 | — | — |
| AIME 2025 | 30.0 | — | — |
| MATH-500 | — | — | — |
| Humanity's Last Exam (with tools) | — | — | — |
| AA-Omniscience Accuracy | 13.6 | — | — |
| AA-LCR v1.1 | 26.3 | — | — |
| CritPt | — | — | — |
| MMMU-Pro | 49.8 | — | — |
| IFBench | 32.0 | — | — |
| Chartography | — | — | — |
| Chartography (With Tools) | — | — | — |
| Coding | |||
| SWE-bench Verified | — | — | — |
| SWE-bench Pro | — | — | — |
| SWE-bench Multilingual | — | — | — |
| LiveCodeBench | 35.1 | — | — |
| Terminal-Bench 2.1 | 9.7 | — | — |
| Aider Polyglot | — | — | — |
| Terminal-Bench 3 | — | — | — |
| BigCodeBench | — | — | — |
| SciCode | 23.8 | — | — |
| CursorBench | — | — | — |
| SWE-Rebench | — | — | — |
| NL2Repo-Bench | — | — | — |
| DeepSWE | — | — | — |
| WebDev Arena | — | — | — |
| Terminal-Bench 4.0 | — | — | — |
| CursorBench 4.0 | — | — | — |
| FrontierCode v1.1 (Main) | — | — | — |
| Arena | |||
| LMArena Elo | — | — | — |
Ministral 3 14B
- Context
- 256K / 33K out
- Parameters
- 14B
- Price
- $0.20 / $0.20
- Speed
- 130 tok/s · 0.2s TTFT
- Modalities
- text, image → text
- License
- Apache 2.0
Largest Ministral 3 edge multimodal model with symmetric $0.20/$0.20 pricing.
Voxtral Small
- Context
- 32K / 8K out
- Parameters
- 24B
- Price
- $0.10 / $0.30
- Speed
- 90 tok/s · 0.35s TTFT
- Modalities
- text, audio → text
- License
- Apache 2.0
Mistral's open audio-understanding model for speech-to-text reasoning and voice agents.
Benchmark charts
Winner bars are emphasized. Per-benchmark deltas sit above each chart.
These two models have no overlapping published benchmarks in our dataset. Compare specs and pricing instead.