Llama-3.3-70B-Instruct vs Mistral Small 3.1 24B vs Nova Pro
Mistral Small 3.1 24B comes out ahead, 55 to 48 and 46 on our weighted score, and it is the cheaper option too.
MetaLlama-3.3-70B-Instruct
46/100- ECI127.3
- Price$0.59 / $0.724
- Context128K
- Our pick
Mistral AIMistral Small 3.1 24B
55/100- ECI127.5
- Price$0.229 / $0.436
- Context128K
AmazonNova Pro
48/100- ECI123.8
- Price$0.80 / $3.20
- Context300K
Mistral Small 3.1 24B is our pick
Mistral Small 3.1 24B is the better all-round choice, scoring 55/100 against Nova Pro (48) and Llama-3.3-70B-Instruct (46). It leads on price. Nova Pro wins on inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityMistral Small 3.1 24BCapabilities Index (ECI): Mistral Small 3.1 24B 127.5 · Llama-3.3-70B-Instruct 127.3 · Nova Pro 123.8
- Lowest priceMistral Small 3.1 24BMistral Small 3.1 24B $0.281 · Llama-3.3-70B-Instruct $0.624 · Nova Pro $1.40 per 1M tokens (3:1 blend)
- Longest contextNova ProNova Pro 300,000 · Llama-3.3-70B-Instruct 128,000 · Mistral Small 3.1 24B 128,000 tokens
- Widest inputsNova ProLlama-3.3-70B-Instruct: Text · Mistral Small 3.1 24B: Text, Images · Nova Pro: Text, Images, PDFs, Video
- Self-hostingLlama-3.3-70B-Instruct and Mistral Small 3.1 24BPublishes downloadable weights
| Measure | Weight | Llama-3.3-70B-Instruct | Mistral Small 3.1 24B | Nova Pro |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 49 | 50 | 45 |
| Price | 25% | 60 | 76 | 43 |
| Inputs & features | 15% | 25 | 60 | 70 |
| Context window | 10% | 24 | 24 | 39 |
| Overall | 100% | 46/100 | 55/100 | 48/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Llama-3.3-70B-InstructMeta | Mistral Small 3.1 24BMistral AI | Nova ProAmazon |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 127.3 | 127.5 (best) | 123.8 |
| ECI rank | #133 of 148 | #132 of 148 (best) | #137 of 148 |
| GPQA DiamondGraduate-level science questions | 47.4% | 47.5% (best) | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 5.1% | 5.8% (best) | — |
| Price per million tokens | |||
| Input | $0.59 | $0.229 (best) | $0.80 |
| Output | $0.724 | $0.436 (best) | $3.20 |
| Cached input | — | — | $0.20 |
| Blended (3:1) | $0.624 | $0.281 (best) | $1.40 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 21 providers | Median of 2 providers | Official Amazon Bedrock API |
| Limits | |||
| Context window | 128,000 tokens | 128,000 tokens | 300,000 tokens (best) |
| Max output | 4,096 tokens | 16,384 tokens (best) | 10,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | llama-3.3-70b-instruct | — | amazon.nova-pro-v1:0 |
| API providers | 24 (best) | 2 | 3 |
| Released | Dec 6, 2024 | Mar 17, 2025 | Dec 3, 2024 |
| Knowledge cutoff | Dec 2023 | Jun 2024 | 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-3.3-70B-Instruct$7.35
- Mistral Small 3.1 24B$3.16
- Nova Pro$14.40
Which should you choose?
Which is better: Llama-3.3-70B-Instruct, Mistral Small 3.1 24B or Nova Pro?
Mistral Small 3.1 24B is the better all-round choice, scoring 55/100 against Nova Pro (48) and Llama-3.3-70B-Instruct (46). It leads on price. Nova Pro wins on inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Llama-3.3-70B-Instruct, Mistral Small 3.1 24B or Nova Pro?
Mistral Small 3.1 24B is cheaper at $0.229 input / $0.436 output per million tokens (median across 2 API providers). Llama-3.3-70B-Instruct costs $0.59 input / $0.724 output per million tokens (median across 21 API providers; free on Llama); Nova Pro costs $0.80 input / $3.20 output per million tokens (official Amazon Bedrock API price). At a typical mix of three input tokens to one output token, that is $0.281 per million tokens for Mistral Small 3.1 24B versus $0.624 for Llama-3.3-70B-Instruct (2.2× as much) and $1.40 for Nova Pro (5× as much).
Which scores higher on benchmarks?
Mistral Small 3.1 24B scores higher on the Capabilities Index (ECI): Mistral Small 3.1 24B 127.5 (#132 of 148), Llama-3.3-70B-Instruct 127.3 (#133 of 148) and Nova Pro 123.8 (#137 of 148). The confidence ranges of the top two overlap (122.6–129.4 vs 122.5–129.5), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Llama-3.3-70B-Instruct, Mistral Small 3.1 24B and Nova Pro yet, so there is no like-for-like coding score. On overall capability, Mistral Small 3.1 24B leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Nova Pro has the largest context window at 300,000 tokens, against 128,000 for Llama-3.3-70B-Instruct and 128,000 for Mistral Small 3.1 24B. Maximum output per response: Llama-3.3-70B-Instruct up to 4,096, Mistral Small 3.1 24B up to 16,384, Nova Pro up to 10,000 tokens.
Which can read images, PDFs, audio or video?
Llama-3.3-70B-Instruct accepts text; Mistral Small 3.1 24B accepts text and images; Nova Pro accepts text, images, PDFs and video. Nova Pro handles the widest range of inputs.
Are any of these open source?
Llama-3.3-70B-Instruct and Mistral Small 3.1 24B publishes its weights and can be self-hosted; Nova Pro is proprietary.
Which is newer?
Mistral Small 3.1 24B is the newest, released Mar 17, 2025. Llama-3.3-70B-Instruct came out Dec 6, 2024; Nova Pro came out Dec 3, 2024. Knowledge cutoff: Llama-3.3-70B-Instruct Dec 2023, Mistral Small 3.1 24B Jun 2024, Nova Pro 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.
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