Mistral Nemo
Efficient Mistral-NVIDIA open model for multilingual chat and local deployment
- Context
- 131K
- Released
- 2y ago
- Parameters
- 12B
- Input / 1M tokens
- $0.03
- Output / 1M tokens
- $0.03
Sources:models.devOpenRouter
Benchmarks
DeepSWE is the primary coding benchmark where an exact model evaluation exists. Other sourced evaluations appear below; missing scores are not zero.
Not evaluated in DeepSWE 1.1 as of 2026-09-22. Results are only shown for exact model versions in the official dataset. Official leaderboard.
No benchmark results have been matched to this model yet.
Specifications
- Developer
- Mistral AI
- Model family
- —
- Type
- Language model
- Predecessor
- —
- Successor
- —
- Released
- July 1, 2024
- Model ID
mistralai/mistral-nemo- Openness
- Open weights
- License
- —
- Parameters
- 12B
- Input modalities
- text
- Output modalities
- text
- Modality
- text->text
- Native context
- 1M
- Tokenizer
- Mistral
- Max output tokens
- 16K
- Knowledge cutoff
- 2024-07
- Hugging Face
- mistralai/Mistral-Nemo-Instruct-2407
- Source freshness
- Metadata synced Oct 8, 2026, 10:17 PM UTC
Open-weight builds
Ollama variants
Every published Ollama tag matched to this model, including parameter editions, instruction/base variants, and quantized builds.
| Variant | Parameters | Type | Quantization | Size | Context | Input |
|---|---|---|---|---|---|---|
| mistral-nemo:12b-instruct-2407-fp16 | 12B | instruct | FP16 | 25 GB | 1M | text |
| mistral-nemo:12b-instruct-2407-q2_K | 12B | instruct | Q2_K | 4.8 GB | 1M | text |
| mistral-nemo:12b-instruct-2407-q3_K_L | 12B | instruct | Q3_K_L | 6.6 GB | 1M | text |
| mistral-nemo:12b-instruct-2407-q3_K_M | 12B | instruct | Q3_K_M | 6.1 GB | 1M | text |
| mistral-nemo:12b-instruct-2407-q3_K_S | 12B | instruct | Q3_K_S | 5.5 GB | 1M | text |
| mistral-nemo:latest | 12B | instruct | Q4_0 | 7.1 GB | 1M | text |
| mistral-nemo:12b-instruct-2407-q4_1 | 12B | instruct | Q4_1 | 7.8 GB | 1M | text |
| mistral-nemo:12b-instruct-2407-q4_K_M | 12B | instruct | Q4_K_M | 7.5 GB | 1M | text |
| mistral-nemo:12b-instruct-2407-q4_K_S | 12B | instruct | Q4_K_S | 7.1 GB | 1M | text |
| mistral-nemo:12b-instruct-2407-q5_0 | 12B | instruct | Q5_0 | 8.5 GB | 1M | text |
| mistral-nemo:12b-instruct-2407-q5_1 | 12B | instruct | Q5_1 | 9.2 GB | 1M | text |
| mistral-nemo:12b-instruct-2407-q5_K_M | 12B | instruct | Q5_K_M | 8.7 GB | 1M | text |
| mistral-nemo:12b-instruct-2407-q5_K_S | 12B | instruct | Q5_K_S | 8.5 GB | 1M | text |
| mistral-nemo:12b-instruct-2407-q6_K | 12B | instruct | Q6_K | 10 GB | 1M | text |
| mistral-nemo:12b-instruct-2407-q8_0 | 12B | instruct | Q8_0 | 13 GB | 1M | text |
Additional source evidence
- Source model
- mistral/mistral-nemo
- Source family
- mistral-nemo
- Last updated
- 2024-07-01
- Knowledge cutoff
- 2024-07
- Model context
- 128K
- Model output limit
- 128K
- Open weights
- Yes
- File attachments
- No
- Reasoning
- No
- Tool calling
- Yes
- Temperature control
- Yes
Downloadable weights
Complete source record (JSON)
{
"id": "mistral/mistral-nemo",
"name": "Mistral Nemo",
"description": "Efficient Mistral-NVIDIA open model for multilingual chat and local deployment",
"family": "mistral-nemo",
"last_updated": "2024-07-01",
"knowledge": "2024-07",
"attachment": false,
"reasoning": false,
"tool_call": true,
"temperature": true,
"modalities": {
"input": [
"text"
],
"output": [
"text"
]
},
"limit": {
"context": 128000,
"output": 128000
},
"release_date": "2024-07-01",
"open_weights": true,
"weights": [
{
"label": "Hugging Face",
"url": "https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407"
}
]
}Infrastructure
Provider availability
| Provider | Endpoint | Context | Input /M tokens | Output /M tokens | Cached input /M tokens | Cache write /M tokens | Uptime 1d | Data handling |
|---|---|---|---|---|---|---|---|---|
| io-net/fp16 | 128K | $0.02 | $0.02 | $0.01 | — | 100.00% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| dekallm/fp8 | 131K | $0.02 | $0.03 | — | — | 100.00% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| deepinfra/fp8 | 131K | $0.03 | $0.03 | — | — | 100.00% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| parasail/fp8 | 131K | $0.03 | $0.03 | — | — | 100.00% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| mistral/eu | 131K | $0.15 | $0.15 | $0.02 | — | 100.00% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 |
Provider data:OpenRouter
Deployment graph
Can I run this?
Vertical Tabs only shows compatibility when it has structured evidence tying this exact model to hardware, runtime, quantization, memory use, and observed performance.