Mistral Large 2.1 vs Gemini 2.0 Flash vs Qwen2.5 72B Instruct
Gemini 2.0 Flash comes out ahead, 65 to 43 and 42 on our weighted score.
Mistral AIMistral Large 2.1
42/100- ECI128.5
- Price$2.00 / $6.00
- Context131K
- Our pick
GoogleGemini 2.0 Flash
65/100- ECI134.7
- Price—
- Context1.05M
Alibaba (Qwen)Qwen2.5 72B Instruct
43/100- ECI129.0
- Price$1.40 / $5.60
- Context131K
Gemini 2.0 Flash is our pick
Gemini 2.0 Flash is the better all-round choice, scoring 65/100 against Qwen2.5 72B Instruct (43) and Mistral Large 2.1 (42). It leads on capability, inputs & features and context window. The score weighs capability 67%, inputs & features 20%, context window 13%.
- CapabilityGemini 2.0 FlashCapabilities Index (ECI): Gemini 2.0 Flash 134.7 · Qwen2.5 72B Instruct 129.0 · Mistral Large 2.1 128.5
- Lowest priceQwen2.5 72B InstructQwen2.5 72B Instruct $2.45 · Mistral Large 2.1 $3.00 per 1M tokens (3:1 blend) · Gemini 2.0 Flash unpriced
- Longest contextGemini 2.0 FlashGemini 2.0 Flash 1,048,576 · Mistral Large 2.1 131,072 · Qwen2.5 72B Instruct 131,072 tokens
- Widest inputsGemini 2.0 FlashMistral Large 2.1: Text · Gemini 2.0 Flash: Text, Images, PDFs, Audio, Video · Qwen2.5 72B Instruct: Text
- Self-hostingMistral Large 2.1 and Qwen2.5 72B InstructPublishes downloadable weights
| Measure | Weight | Mistral Large 2.1 | Gemini 2.0 Flash | Qwen2.5 72B Instruct |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 67% | 51 | 59 | 52 |
| Inputs & features | 20% | 25 | 90 | 25 |
| Context window | 13% | 24 | 61 | 24 |
| Overall | 100% | 42/100 | 65/100 | 43/100 |
Left out because at least one model lacks the data: price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Mistral Large 2.1Mistral AI | Gemini 2.0 FlashGoogle | Qwen2.5 72B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 128.5 | 134.7 (best) | 129.0 |
| ECI rank | #130 of 148 | #116 of 148 (best) | #128 of 148 |
| GPQA DiamondGraduate-level science questions | 51.3% (best) | — | 49.2% |
| OTIS Mock AIME 2024–2025Competition mathematics | 7.8% | — | 8.1% (best) |
| Price per million tokens | |||
| Input | $2.00 | — | $1.40 (best) |
| Output | $6.00 | — | $5.60 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $3.00 | — | $2.45 (best) |
| Long-context rate | Same rate | — | Same rate |
| Price source | Official Mistral API | — | Official Alibaba API |
| Limits | |||
| Context window | 131,072 tokens | 1,048,576 tokens (best) | 131,072 tokens |
| Max output | 16,384 tokens (best) | 8,192 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | Yes | No |
| Audio | No | Yes | No |
| Video | No | Yes | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | mistral-large-2411 | — | qwen2-5-72b-instruct |
| API providers | 2 (best) | — | 1 |
| Released | Nov 18, 2024 | Dec 11, 2024 | Sep 19, 2024 |
| Knowledge cutoff | Nov 2024 | Jun 2024 | Apr 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Mistral Large 2.1$32.00
- Gemini 2.0 Flash—
- Qwen2.5 72B Instruct$25.20
Which should you choose?
Which is better: Mistral Large 2.1, Gemini 2.0 Flash or Qwen2.5 72B Instruct?
Gemini 2.0 Flash is the better all-round choice, scoring 65/100 against Qwen2.5 72B Instruct (43) and Mistral Large 2.1 (42). It leads on capability, inputs & features and context window. The score weighs capability 67%, inputs & features 20%, context window 13%.
Which is cheaper, Mistral Large 2.1, Gemini 2.0 Flash or Qwen2.5 72B Instruct?
Qwen2.5 72B Instruct is cheaper at $1.40 input / $5.60 output per million tokens (official Alibaba API price). Mistral Large 2.1 costs $2.00 input / $6.00 output per million tokens (official Mistral API price). At a typical mix of three input tokens to one output token, that is $2.45 per million tokens for Qwen2.5 72B Instruct versus $3.00 for Mistral Large 2.1 (1.2× as much). Gemini 2.0 Flash has no published per-token price.
Which scores higher on benchmarks?
Gemini 2.0 Flash scores higher on the Capabilities Index (ECI): Gemini 2.0 Flash 134.7 (#116 of 148), Qwen2.5 72B Instruct 129.0 (#128 of 148) and Mistral Large 2.1 128.5 (#130 of 148). The confidence ranges of the top two overlap (124.3–136.7 vs 123.8–130.7), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Mistral Large 2.1, Gemini 2.0 Flash and Qwen2.5 72B Instruct yet, so there is no like-for-like coding score. On overall capability, Gemini 2.0 Flash 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?
Gemini 2.0 Flash has the largest context window at 1,048,576 tokens, against 131,072 for Mistral Large 2.1 and 131,072 for Qwen2.5 72B Instruct. Maximum output per response: Mistral Large 2.1 up to 16,384, Gemini 2.0 Flash up to 8,192, Qwen2.5 72B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Mistral Large 2.1 accepts text; Gemini 2.0 Flash accepts text, images, PDFs, audio and video; Qwen2.5 72B Instruct accepts text. Gemini 2.0 Flash handles the widest range of inputs.
Are any of these open source?
Mistral Large 2.1 and Qwen2.5 72B Instruct publishes its weights and can be self-hosted; Gemini 2.0 Flash is proprietary.
Which is newer?
Gemini 2.0 Flash is the newest, released Dec 11, 2024. Mistral Large 2.1 came out Nov 18, 2024; Qwen2.5 72B Instruct came out Sep 19, 2024. Knowledge cutoff: Mistral Large 2.1 Nov 2024, Gemini 2.0 Flash Jun 2024, Qwen2.5 72B Instruct Apr 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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