Gemini 2.5 Flash-Lite vs Llama 3.1 Nemotron Ultra 253B vs Mistral Small 3.2
Gemini 2.5 Flash-Lite comes out ahead, 85 to 65 and 64 on our weighted score.
- Our pick
Google
Gemini 2.5 Flash-Lite
85/100- ECI133.9
- Price$0.10 / $0.40
- Context1.05M
NVIDIA
Llama 3.1 Nemotron Ultra 253B
65/100- ECI—
- PriceFree / Free
- Context128K
Mistral AI
Mistral Small 3.2
64/100- ECI131.7
- Price$0.10 / $0.30
- Context128K
Gemini 2.5 Flash-Lite is our pick
Gemini 2.5 Flash-Lite is the better all-round choice, scoring 85/100 against Llama 3.1 Nemotron Ultra 253B (65) and Mistral Small 3.2 (64). It leads on inputs & features and context window. Llama 3.1 Nemotron Ultra 253B wins on price. 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 priceLlama 3.1 Nemotron Ultra 253BLlama 3.1 Nemotron Ultra 253B Free · Mistral Small 3.2 $0.15 · Gemini 2.5 Flash-Lite $0.175 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 Flash-LiteGemini 2.5 Flash-Lite 1,048,576 · Llama 3.1 Nemotron Ultra 253B 128,000 · Mistral Small 3.2 128,000 tokens
- Widest inputsGemini 2.5 Flash-LiteGemini 2.5 Flash-Lite: Text, Images, PDFs, Audio, Video · Llama 3.1 Nemotron Ultra 253B: Text · Mistral Small 3.2: Text, Images
- Self-hostingLlama 3.1 Nemotron Ultra 253B and Mistral Small 3.2Publishes downloadable weights
| Measure | Weight | Gemini 2.5 Flash-Lite | Llama 3.1 Nemotron Ultra 253B | Mistral Small 3.2 |
|---|---|---|---|---|
| Price | 50% | 86 | 100 | 89 |
| Inputs & features | 30% | 100 | 35 | 50 |
| Context window | 20% | 61 | 24 | 24 |
| Overall | 100% | 85/100 | 65/100 | 64/100 |
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 | |||
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 133.9 (best) | — | 131.7 |
| ECI rank | #118 of 148 (best) | — | #123 of 148 |
| GPQA DiamondGraduate-level science questions | — | — | 49.1% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 30.3% |
| Price per million tokens | |||
| Input | $0.10 | Free (best) | $0.10 |
| Output | $0.40 | Free (best) | $0.30 |
| Cached input | $0.01 | — | — |
| Blended (3:1) | $0.175 | Free (best) | $0.15 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Google API | Official Nvidia API | Official Mistral API |
| Limits | |||
| Context window | 1,048,576 tokens (best) | 128,000 tokens | 128,000 tokens |
| Max output | 65,536 tokens (best) | 8,192 tokens | 16,384 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | Yes | No | No |
| Audio | Yes | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | gemini-2.5-flash-lite | nvidia/llama-3.1-nemotron-ultra-253b-v1 | mistral-small-2506 |
| API providers | 20 (best) | 1 | 6 |
| Released | Jun 17, 2025 | Apr 7, 2025 | Jun 20, 2025 |
| Knowledge cutoff | Jan 2025 | — | Mar 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
Gemini 2.5 Flash-Lite$1.80
Llama 3.1 Nemotron Ultra 253BFree
Mistral Small 3.2$1.60
Which should you choose?
Which is better: Gemini 2.5 Flash-Lite, Llama 3.1 Nemotron Ultra 253B or Mistral Small 3.2?
Gemini 2.5 Flash-Lite is the better all-round choice, scoring 85/100 against Llama 3.1 Nemotron Ultra 253B (65) and Mistral Small 3.2 (64). It leads on inputs & features and context window. Llama 3.1 Nemotron Ultra 253B wins on price. 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, Gemini 2.5 Flash-Lite, Llama 3.1 Nemotron Ultra 253B or Mistral Small 3.2?
Llama 3.1 Nemotron Ultra 253B is cheaper at Free input / Free output per million tokens (official Nvidia API price). Mistral Small 3.2 costs $0.10 input / $0.30 output per million tokens (official Mistral API price); Gemini 2.5 Flash-Lite costs $0.10 input / $0.40 output per million tokens (official Google API price). Llama 3.1 Nemotron Ultra 253B is listed as free.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Gemini 2.5 Flash-Lite has an ECI of 133.9, Llama 3.1 Nemotron Ultra 253B has not been scored yet and Mistral Small 3.2 has an ECI of 131.7.
Which is better for coding?
There are no published SWE-bench Verified results for Gemini 2.5 Flash-Lite, Llama 3.1 Nemotron Ultra 253B and Mistral Small 3.2 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?
Gemini 2.5 Flash-Lite has the largest context window at 1,048,576 tokens, against 128,000 for Llama 3.1 Nemotron Ultra 253B and 128,000 for Mistral Small 3.2. Maximum output per response: Gemini 2.5 Flash-Lite up to 65,536, Llama 3.1 Nemotron Ultra 253B up to 8,192, Mistral Small 3.2 up to 16,384 tokens.
Which can read images, PDFs, audio or video?
Gemini 2.5 Flash-Lite accepts text, images, PDFs, audio and video; Llama 3.1 Nemotron Ultra 253B accepts text; Mistral Small 3.2 accepts text and images. Gemini 2.5 Flash-Lite handles the widest range of inputs.
Are any of these open source?
Llama 3.1 Nemotron Ultra 253B and Mistral Small 3.2 publishes its weights and can be self-hosted; Gemini 2.5 Flash-Lite is proprietary.
Which is newer?
Mistral Small 3.2 is the newest, released Jun 20, 2025. Gemini 2.5 Flash-Lite came out Jun 17, 2025; Llama 3.1 Nemotron Ultra 253B came out Apr 7, 2025. Knowledge cutoff: Gemini 2.5 Flash-Lite Jan 2025, Mistral Small 3.2 Mar 2025.
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.