GLM-5 vs Gemini 2.5 Pro vs Qwen3.5 397B-A17B
Too close to call on our weighted score (Qwen3.5 397B-A17B 65, Gemini 2.5 Pro 63, GLM-5 55). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-5
55/100- ECI145.8
- Price$1.00 / $3.20
- Context205K
GoogleGemini 2.5 Pro
63/100- ECI145.3
- Price$1.25 / $10.00
- Context1.05M
Alibaba (Qwen)Qwen3.5 397B-A17B
65/100- ECI146.7
- Price$0.60 / $3.60
- Context262K
Too close to call
It is close. Our weighted score puts them within 2 points (Qwen3.5 397B-A17B 65/100, Gemini 2.5 Pro 63/100, GLM-5 55/100), so choose by what matters most for your work: Qwen3.5 397B-A17B for raw capability and Gemini 2.5 Pro for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3.5 397B-A17BCapabilities Index (ECI): Qwen3.5 397B-A17B 146.7 · GLM-5 145.8 · Gemini 2.5 Pro 145.3
- Lowest priceQwen3.5 397B-A17BQwen3.5 397B-A17B $1.35 · GLM-5 $1.55 · Gemini 2.5 Pro $3.44 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 ProGemini 2.5 Pro 1,048,576 · Qwen3.5 397B-A17B 262,144 · GLM-5 204,800 tokens
- Widest inputsGemini 2.5 ProGLM-5: Text · Gemini 2.5 Pro: Text, Images, PDFs, Audio, Video · Qwen3.5 397B-A17B: Text, Images, Audio, Video
- Self-hostingGLM-5 and Qwen3.5 397B-A17BPublishes downloadable weights
| Measure | Weight | GLM-5 | Gemini 2.5 Pro | Qwen3.5 397B-A17B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 73 | 72 | 74 |
| Price | 25% | 41 | 24 | 44 |
| Inputs & features | 15% | 35 | 100 | 90 |
| Context window | 10% | 32 | 61 | 37 |
| Overall | 100% | 55/100 | 63/100 | 65/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5Z.ai (Zhipu) | Gemini 2.5 ProGoogle | Qwen3.5 397B-A17BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.8 | 145.3 | 146.7 (best) |
| ECI rank | #74 of 148 | #78 of 148 | #67 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 87.8% (best) | 85.3% | 86.4% |
| FrontierMath Tiers 1–3Research-level mathematics | — | 24.6% | 31.2% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 80.0% | 84.7% | 88.9% (best) |
| SWE-bench VerifiedFixing real GitHub issues | 72.1% (best) | 57.6% | — |
| Price per million tokens | |||
| Input | $1.00 | $1.25 | $0.60 (best) |
| Output | $3.20 (best) | $10.00 | $3.60 |
| Cached input | $0.20 | $0.125 (best) | — |
| Blended (3:1) | $1.55 | $3.44 | $1.35 (best) |
| Long-context rate | Same rate | Over 200K: $2.50 / $15.00 | Same rate |
| Price source | Official Z.AI API | Official Google API | Official Alibaba API |
| Limits | |||
| Context window | 204,800 tokens | 1,048,576 tokens (best) | 262,144 tokens |
| Max output | 131,072 tokens (best) | 65,536 tokens | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | Yes | No |
| Audio | No | Yes | Yes |
| Video | No | Yes | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | glm-5 | gemini-2.5-pro | qwen3.5-397b-a17b |
| API providers | 27 (best) | 22 | 23 |
| Released | Feb 12, 2026 | Jun 17, 2025 | Feb 15, 2026 |
| Knowledge cutoff | — | Jan 2025 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GLM-5$16.40
- Gemini 2.5 Pro$32.50
- Qwen3.5 397B-A17B$13.20
Which should you choose?
Which is better: GLM-5, Gemini 2.5 Pro or Qwen3.5 397B-A17B?
It is close. Our weighted score puts them within 2 points (Qwen3.5 397B-A17B 65/100, Gemini 2.5 Pro 63/100, GLM-5 55/100), so choose by what matters most for your work: Qwen3.5 397B-A17B for raw capability and Gemini 2.5 Pro for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5, Gemini 2.5 Pro or Qwen3.5 397B-A17B?
Qwen3.5 397B-A17B is cheaper at $0.60 input / $3.60 output per million tokens (official Alibaba API price). GLM-5 costs $1.00 input / $3.20 output per million tokens (official Z.AI API price); Gemini 2.5 Pro costs $1.25 input / $10.00 output per million tokens (official Google API price). At a typical mix of three input tokens to one output token, that is $1.35 per million tokens for Qwen3.5 397B-A17B versus $1.55 for GLM-5 (1.1× as much) and $3.44 for Gemini 2.5 Pro (2.5× as much).
Which scores higher on benchmarks?
Qwen3.5 397B-A17B scores higher on the Capabilities Index (ECI): Qwen3.5 397B-A17B 146.7 (#67 of 148), GLM-5 145.8 (#74 of 148) and Gemini 2.5 Pro 145.3 (#78 of 148). The confidence ranges of the top two overlap (144.8–148.2 vs 143.9–147.7), so treat the gap as small. On individual benchmarks: GPQA Diamond — GLM-5 87.8%, Qwen3.5 397B-A17B 86.4%, Gemini 2.5 Pro 85.3%; OTIS Mock AIME 2024–2025 — Qwen3.5 397B-A17B 88.9%, Gemini 2.5 Pro 84.7%, GLM-5 80.0%.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3.5 397B-A17B yet, so there is no like-for-like coding score. On overall capability, Qwen3.5 397B-A17B 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 2.5 Pro has the largest context window at 1,048,576 tokens, against 262,144 for Qwen3.5 397B-A17B and 204,800 for GLM-5. Maximum output per response: GLM-5 up to 131,072, Gemini 2.5 Pro up to 65,536, Qwen3.5 397B-A17B up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GLM-5 accepts text; Gemini 2.5 Pro accepts text, images, PDFs, audio and video; Qwen3.5 397B-A17B accepts text, images, audio and video. Gemini 2.5 Pro handles the widest range of inputs.
Are any of these open source?
GLM-5 and Qwen3.5 397B-A17B publishes its weights and can be self-hosted; Gemini 2.5 Pro is proprietary.
Which is newer?
Qwen3.5 397B-A17B is the newest, released Feb 15, 2026. GLM-5 came out Feb 12, 2026; Gemini 2.5 Pro came out Jun 17, 2025. Knowledge cutoff: Gemini 2.5 Pro Jan 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.
