Pixtral Large (25.02) vs GLM-4.7 vs Gemini 2.5 Pro Preview 06-05
Gemini 2.5 Pro Preview 06-05 comes out ahead, 48 to 41 and 33 on our weighted score, though GLM-4.7 is 3.4× cheaper per token.
Mistral AIPixtral Large (25.02)
33/100- ECI—
- Price$2.00 / $6.00
- Context128K
Z.ai (Zhipu)GLM-4.7
41/100- ECI143.7
- Price$0.60 / $2.20
- Context205K
- Our pick
GoogleGemini 2.5 Pro Preview 06-05
48/100- ECI—
- Price$1.22 / $9.75
- Context1.05M
Gemini 2.5 Pro Preview 06-05 is our pick
Gemini 2.5 Pro Preview 06-05 is the better all-round choice, scoring 48/100 against GLM-4.7 (41) and Pixtral Large (25.02) (33). It leads on inputs & features and context window. GLM-4.7 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 priceGLM-4.7GLM-4.7 $1.00 · Pixtral Large (25.02) $3.00 · Gemini 2.5 Pro Preview 06-05 $3.35 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 Pro Preview 06-05Gemini 2.5 Pro Preview 06-05 1,048,576 · GLM-4.7 204,800 · Pixtral Large (25.02) 128,000 tokens
- Widest inputsGemini 2.5 Pro Preview 06-05Pixtral Large (25.02): Text, Images · GLM-4.7: Text · Gemini 2.5 Pro Preview 06-05: Text, Images, PDFs, Audio
- Self-hostingGLM-4.7Publishes downloadable weights
| Measure | Weight | Pixtral Large (25.02) | GLM-4.7 | Gemini 2.5 Pro Preview 06-05 |
|---|---|---|---|---|
| Price | 50% | 26 | 48 | 24 |
| Inputs & features | 30% | 50 | 35 | 80 |
| Context window | 20% | 24 | 32 | 61 |
| Overall | 100% | 33/100 | 41/100 | 48/100 |
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 | Pixtral Large (25.02)Mistral AI | GLM-4.7Z.ai (Zhipu) | Gemini 2.5 Pro Preview 06-05Google |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 143.7 | — |
| ECI rank | — | #94 of 165 | — |
| GPQA DiamondGraduate-level science questions | — | 83.3% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 83.3% | — |
| SimpleQA VerifiedShort factual questions | — | 32.2% | — |
| Price per million tokens | |||
| Input | $2.00 | $0.60 (best) | $1.22 |
| Output | $6.00 | $2.20 (best) | $9.75 |
| Cached input | — | $0.11 | — |
| Blended (3:1) | $3.00 | $1.00 (best) | $3.35 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 3 providers | Official Z.AI API | Median of 4 providers |
| Limits | |||
| Context window | 128,000 tokens | 204,800 tokens | 1,048,576 tokens (best) |
| Max output | 8,192 tokens | 131,072 tokens (best) | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | Yes |
| Video | No | No | No |
| Reasoning | No | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | — | glm-4.7 | — |
| API providers | 3 | 31 (best) | 4 |
| Released | Apr 8, 2025 | Dec 22, 2025 | Jun 5, 2025 |
| Knowledge cutoff | — | Apr 2025 | Jan 31, 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Pixtral Large (25.02)$32.00
- GLM-4.7$10.40
- Gemini 2.5 Pro Preview 06-05$31.69
Which should you choose?
Which is better: Pixtral Large (25.02), GLM-4.7 or Gemini 2.5 Pro Preview 06-05?
Gemini 2.5 Pro Preview 06-05 is the better all-round choice, scoring 48/100 against GLM-4.7 (41) and Pixtral Large (25.02) (33). It leads on inputs & features and context window. GLM-4.7 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, Pixtral Large (25.02), GLM-4.7 or Gemini 2.5 Pro Preview 06-05?
GLM-4.7 is cheaper at $0.60 input / $2.20 output per million tokens (official Z.AI API price). Pixtral Large (25.02) costs $2.00 input / $6.00 output per million tokens (median across 3 API providers); Gemini 2.5 Pro Preview 06-05 costs $1.22 input / $9.75 output per million tokens (median across 4 API providers). At a typical mix of three input tokens to one output token, that is $1.00 per million tokens for GLM-4.7 versus $3.00 for Pixtral Large (25.02) (3× as much) and $3.35 for Gemini 2.5 Pro Preview 06-05 (3.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Pixtral Large (25.02) has not been scored yet, GLM-4.7 has an ECI of 143.7 and Gemini 2.5 Pro Preview 06-05 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Pixtral Large (25.02), GLM-4.7 and Gemini 2.5 Pro Preview 06-05 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 Pro Preview 06-05 has the largest context window at 1,048,576 tokens, against 204,800 for GLM-4.7 and 128,000 for Pixtral Large (25.02). Maximum output per response: Pixtral Large (25.02) up to 8,192, GLM-4.7 up to 131,072, Gemini 2.5 Pro Preview 06-05 up to 65,536 tokens.
Which can read images, PDFs, audio or video?
Pixtral Large (25.02) accepts text and images; GLM-4.7 accepts text; Gemini 2.5 Pro Preview 06-05 accepts text, images, PDFs and audio. Gemini 2.5 Pro Preview 06-05 handles the widest range of inputs.
Are any of these open source?
GLM-4.7 publishes its weights and can be self-hosted; Pixtral Large (25.02) and Gemini 2.5 Pro Preview 06-05 is proprietary.
Which is newer?
GLM-4.7 is the newest, released Dec 22, 2025. Gemini 2.5 Pro Preview 06-05 came out Jun 5, 2025; Pixtral Large (25.02) came out Apr 8, 2025. Knowledge cutoff: GLM-4.7 Apr 2025, Gemini 2.5 Pro Preview 06-05 Jan 31, 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 Nov 2, 2026.
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