vs
3:0
overall capability wins (0 ties across 3 scored). Ministral 3 14B leads overall.
- Pricing0:3
- Speed0:1
- Specs3:0
Verdict
Ministral 3 14B offers ~2.0x larger context (256K vs 128K); Ministral 3 14B allows ~4.0x more max output (33K vs 8K); Mistral Nemo 12B is ~1.3x cheaper on a blended token basis than Ministral 3 14B.
- Ministral 3 14B offers ~2.0x larger context (256K vs 128K)
- Ministral 3 14B allows ~4.0x more max output (33K vs 8K)
- Mistral Nemo 12B 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
- Mistral Nemo 12B
- $0.19
Mistral Nemo 12B is about 1.3x 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 18, 2024
Knowledge cutoff
-
-
API / provider
Mistral
Mistral
Modalities
text, image → text
text → text
Specs
Context window
256K
128K
Max output
33K
8K
Parameters
14B
12B
Pricing
Input $/1M
$0.20
$0.15
Output $/1M
$0.20
$0.15
Blended $/1M (3∶1)
$0.20
$0.15
Speed
tok/s
130
140
TTFT (s)
0.2
0.2
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 | Mistral Nemo 12B | Delta |
|---|---|---|---|
| Identity | |||
| Organization | Mistral | Mistral | — |
| Family | Mistral | Mistral | — |
| License | Apache 2.0 | Apache 2.0 | — |
| Open weights | Yes | Yes | — |
| Release | Dec 1, 2025 | Jul 18, 2024 | — |
| Knowledge cutoff | - | - | — |
| API / provider | Mistral | Mistral | — |
| Modalities | text, image → text | text → text | — |
| Specs | |||
| Context window | 256K | 128K | A +128K |
| Max output | 33K | 8K | A +25K |
| Parameters | 14B | 12B | A +2.00 |
| Pricing | |||
| Input $/1M | $0.20 | $0.15 | B +$0.05 |
| Output $/1M | $0.20 | $0.15 | B +$0.05 |
| Blended $/1M (3∶1) | $0.20 | $0.15 | B +$0.05 |
| Speed | |||
| tok/s | 130 | 140 | B +10 |
| TTFT (s) | 0.2 | 0.2 | tie |
| 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.
Mistral Nemo 12B
- Context
- 128K / 8K out
- Parameters
- 12B
- Price
- $0.15 / $0.15
- Speed
- 140 tok/s · 0.2s TTFT
- Modalities
- text → text
- License
- Apache 2.0
Compact open Mistral x NVIDIA collab model - popular edge and multilingual baseline.
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.