GPT-5.1 Codex mini vs Nova Premier vs Mistral Large 3
GPT-5.1 Codex mini comes out ahead, 59 to 50 and 41 on our weighted score, and it is the cheaper option too.
- Our pick
OpenAIGPT-5.1 Codex mini
59/100- ECI—
- Price$0.25 / $2.00
- Context400K
AmazonNova Premier
41/100- ECI—
- Price$2.50 / $12.50
- Context1M
Mistral AIMistral Large 3
50/100- ECI—
- Price$0.50 / $1.50
- Context262K
GPT-5.1 Codex mini is our pick
GPT-5.1 Codex mini is the better all-round choice, scoring 59/100 against Mistral Large 3 (50) and Nova Premier (41). It leads on price. Nova Premier wins on context window. 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 priceGPT-5.1 Codex miniGPT-5.1 Codex mini $0.688 · Mistral Large 3 $0.75 · Nova Premier $5.00 per 1M tokens (3:1 blend)
- Longest contextNova PremierNova Premier 1,000,000 · GPT-5.1 Codex mini 400,000 · Mistral Large 3 262,144 tokens
- Widest inputsNova PremierGPT-5.1 Codex mini: Text, Images · Nova Premier: Text, Images, PDFs, Video · Mistral Large 3: Text, Images
- Self-hostingMistral Large 3Publishes downloadable weights
| Measure | Weight | GPT-5.1 Codex mini | Nova Premier | Mistral Large 3 |
|---|---|---|---|---|
| Price | 50% | 58 | 17 | 56 |
| Inputs & features | 30% | 70 | 70 | 50 |
| Context window | 20% | 44 | 60 | 37 |
| Overall | 100% | 59/100 | 41/100 | 50/100 |
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 | GPT-5.1 Codex miniOpenAI | Nova PremierAmazon | Mistral Large 3Mistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.25 (best) | $2.50 | $0.50 |
| Output | $2.00 | $12.50 | $1.50 (best) |
| Cached input | — | $0.625 | $0.05 (best) |
| Blended (3:1) | $0.688 (best) | $5.00 | $0.75 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 10 providers | Official Amazon Bedrock API | Official Mistral API |
| Limits | |||
| Context window | 400,000 tokens | 1,000,000 tokens (best) | 262,144 tokens |
| Max output | 128,000 tokens | 10,000 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | Yes | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | — | us.amazon.nova-premier-v1:0 | mistral-large-2512 |
| API providers | 10 | 1 | 13 (best) |
| Released | Nov 13, 2025 | Apr 30, 2025 | Dec 2, 2025 |
| Knowledge cutoff | Sep 30, 2024 | Oct 2024 | Nov 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GPT-5.1 Codex mini$6.50
- Nova Premier$50.00
- Mistral Large 3$8.00
Which should you choose?
Which is better: GPT-5.1 Codex mini, Nova Premier or Mistral Large 3?
GPT-5.1 Codex mini is the better all-round choice, scoring 59/100 against Mistral Large 3 (50) and Nova Premier (41). It leads on price. Nova Premier wins on context window. 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, GPT-5.1 Codex mini, Nova Premier or Mistral Large 3?
GPT-5.1 Codex mini is cheaper at $0.25 input / $2.00 output per million tokens (median across 10 API providers). Mistral Large 3 costs $0.50 input / $1.50 output per million tokens (official Mistral API price); Nova Premier costs $2.50 input / $12.50 output per million tokens (official Amazon Bedrock API price). At a typical mix of three input tokens to one output token, that is $0.688 per million tokens for GPT-5.1 Codex mini versus $0.75 for Mistral Large 3 (1.1× as much) and $5.00 for Nova Premier (7.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GPT-5.1 Codex mini has not been scored yet, Nova Premier has not been scored yet and Mistral Large 3 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5.1 Codex mini, Nova Premier and Mistral Large 3 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Nova Premier has the largest context window at 1,000,000 tokens, against 400,000 for GPT-5.1 Codex mini and 262,144 for Mistral Large 3. Maximum output per response: GPT-5.1 Codex mini up to 128,000, Nova Premier up to 10,000, Mistral Large 3 up to 262,144 tokens.
Which can read images, PDFs, audio or video?
GPT-5.1 Codex mini accepts text and images; Nova Premier accepts text, images, PDFs and video; Mistral Large 3 accepts text and images. Nova Premier handles the widest range of inputs.
Are any of these open source?
Mistral Large 3 publishes its weights and can be self-hosted; GPT-5.1 Codex mini and Nova Premier is proprietary.
Which is newer?
Mistral Large 3 is the newest, released Dec 2, 2025. GPT-5.1 Codex mini came out Nov 13, 2025; Nova Premier came out Apr 30, 2025. Knowledge cutoff: GPT-5.1 Codex mini Sep 30, 2024, Nova Premier Oct 2024, Mistral Large 3 Nov 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.
