Llama-3.2-1B vs Nova Micro vs Ministral 8B Instruct
Nova Micro comes out ahead, 62 to 57 and 55 on our weighted score, and it is the cheaper option too.
MetaLlama-3.2-1B
55/100- ECI102.0
- Price$0.064 / $0.15
- Context131K
- Our pick
AmazonNova Micro
62/100- ECI—
- Price$0.035 / $0.14
- Context128K
Mistral AIMinistral 8B Instruct
57/100- ECI—
- Price$0.15 / $0.15
- Context131K
Nova Micro is our pick
Nova Micro is the better all-round choice, scoring 62/100 against Ministral 8B Instruct (57) and Llama-3.2-1B (55). The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceNova MicroNova Micro $0.061 · Llama-3.2-1B $0.085 · Ministral 8B Instruct $0.15 per 1M tokens (3:1 blend)
- Longest contextLlama-3.2-1B and Ministral 8B InstructLlama-3.2-1B 131,072 · Ministral 8B Instruct 131,072 · Nova Micro 128,000 tokens
- Widest inputsSame inputsLlama-3.2-1B: Text · Nova Micro: Text · Ministral 8B Instruct: Text
- Self-hostingLlama-3.2-1B and Ministral 8B InstructPublishes downloadable weights (Llama 3.2 Community License and Mistral Research License)
| Measure | Weight | Llama-3.2-1B | Nova Micro | Ministral 8B Instruct |
|---|---|---|---|---|
| Price | 50% | 100 | 100 | 89 |
| Inputs & features | 30% | 0 | 25 | 25 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 55/100 | 62/100 | 57/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Llama-3.2-1BMeta | Nova MicroAmazon | Ministral 8B InstructMistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 102.0 | — | — |
| ECI rank | #147 of 148 | — | — |
| GPQA DiamondGraduate-level science questions | 23.9% | — | 27.2% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 0.6% | — | — |
| Price per million tokens | |||
| Input | $0.064 | $0.035 (best) | $0.15 |
| Output | $0.15 | $0.14 (best) | $0.15 |
| Cached input | — | $0.0088 | — |
| Blended (3:1) | $0.085 | $0.061 (best) | $0.15 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Official Amazon Bedrock API | Median of 1 providers |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 131,072 tokens (best) |
| Max output | 8,192 tokens | 10,000 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenLlama 3.2 Community License | Proprietary | OpenMistral Research License |
| API model ID | — | amazon.nova-micro-v1:0 | — |
| API providers | 2 | 3 (best) | 1 |
| Released | Sep 25, 2024 | Dec 3, 2024 | Oct 16, 2024 |
| Knowledge cutoff | Dec 2023 | Oct 2024 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Llama-3.2-1B$0.936
- Nova Micro$0.63
- Ministral 8B Instruct$1.80
Which should you choose?
Which is better: Llama-3.2-1B, Nova Micro or Ministral 8B Instruct?
Nova Micro is the better all-round choice, scoring 62/100 against Ministral 8B Instruct (57) and Llama-3.2-1B (55). The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Llama-3.2-1B, Nova Micro or Ministral 8B Instruct?
Nova Micro is cheaper at $0.035 input / $0.14 output per million tokens (official Amazon Bedrock API price). Llama-3.2-1B costs $0.064 input / $0.15 output per million tokens (median across 2 API providers); Ministral 8B Instruct costs $0.15 input / $0.15 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.061 per million tokens for Nova Micro versus $0.085 for Llama-3.2-1B (1.4× as much) and $0.15 for Ministral 8B Instruct (2.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Llama-3.2-1B has an ECI of 102.0, Nova Micro has not been scored yet and Ministral 8B Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Llama-3.2-1B, Nova Micro and Ministral 8B Instruct yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Llama-3.2-1B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Llama-3.2-1B and Ministral 8B Instruct have the largest context windows (131,072 and 131,072 tokens), against 128,000 for Nova Micro. Maximum output per response: Llama-3.2-1B up to 8,192, Nova Micro up to 10,000, Ministral 8B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Llama-3.2-1B accepts text; Nova Micro accepts text; Ministral 8B Instruct accepts text. They handle the same number of input types.
Are any of these open source?
Llama-3.2-1B and Ministral 8B Instruct publishes its weights (Llama 3.2 Community License and Mistral Research License) and can be self-hosted; Nova Micro is proprietary.
Which is newer?
Nova Micro is the newest, released Dec 3, 2024. Ministral 8B Instruct came out Oct 16, 2024; Llama-3.2-1B came out Sep 25, 2024. Knowledge cutoff: Llama-3.2-1B Dec 2023, Nova Micro Oct 2024.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
