GLM-5 vs GLM-5.1 vs Qwen3.5 397B-A17B
Qwen3.5 397B-A17B comes out ahead, 65 to 57 and 55 on our weighted score, and it is the cheaper option too.
Z.ai (Zhipu)GLM-5
55/100- ECI145.8
- Price$1.00 / $3.20
- Context205K
- Z.ai (Zhipu)
GLM-5.1
57/100- ECI149.9
- Price$1.40 / $4.40
- Context200K
- Our pick
Alibaba (Qwen)Qwen3.5 397B-A17B
65/100- ECI146.7
- Price$0.60 / $3.60
- Context262K
Qwen3.5 397B-A17B is our pick
Qwen3.5 397B-A17B is the better all-round choice, scoring 65/100 against GLM-5.1 (57) and GLM-5 (55). It leads on price, inputs & features and context window. GLM-5.1 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGLM-5.1Capabilities Index (ECI): GLM-5.1 149.9 · Qwen3.5 397B-A17B 146.7 · GLM-5 145.8
- Lowest priceQwen3.5 397B-A17BQwen3.5 397B-A17B $1.35 · GLM-5 $1.55 · GLM-5.1 $2.15 per 1M tokens (3:1 blend)
- Longest contextQwen3.5 397B-A17BQwen3.5 397B-A17B 262,144 · GLM-5 204,800 · GLM-5.1 200,000 tokens
- Widest inputsQwen3.5 397B-A17BGLM-5: Text · GLM-5.1: Text · Qwen3.5 397B-A17B: Text, Images, Audio, Video
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-5 | GLM-5.1 | Qwen3.5 397B-A17B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 73 | 78 | 74 |
| Price | 25% | 41 | 34 | 44 |
| Inputs & features | 15% | 35 | 45 | 90 |
| Context window | 10% | 32 | 32 | 37 |
| Overall | 100% | 55/100 | 57/100 | 65/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5Z.ai (Zhipu) | GLM-5.1Z.ai (Zhipu) | Qwen3.5 397B-A17BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.8 | 149.9 (best) | 146.7 |
| ECI rank | #74 of 148 | #51 of 148 (best) | #67 of 148 |
| GPQA DiamondGraduate-level science questions | 87.8% | 89.9% (best) | 86.4% |
| FrontierMath Tiers 1–3Research-level mathematics | — | 36.8% (best) | 31.2% |
| OTIS Mock AIME 2024–2025Competition mathematics | 80.0% | 93.3% (best) | 88.9% |
| SWE-bench VerifiedFixing real GitHub issues | 72.1% | 74.2% (best) | — |
| SimpleQA VerifiedShort factual questions | — | 34.0% | — |
| Price per million tokens | |||
| Input | $1.00 | $1.40 | $0.60 (best) |
| Output | $3.20 (best) | $4.40 | $3.60 |
| Cached input | $0.20 (best) | $0.26 | — |
| Blended (3:1) | $1.55 | $2.15 | $1.35 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Z.AI API | Official Alibaba API |
| Limits | |||
| Context window | 204,800 tokens | 200,000 tokens | 262,144 tokens (best) |
| Max output | 131,072 tokens (best) | 131,072 tokens (best) | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | Yes |
| Video | No | No | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-5 | glm-5.1 | qwen3.5-397b-a17b |
| API providers | 27 | 40 (best) | 23 |
| Released | Feb 12, 2026 | Apr 7, 2026 | Feb 15, 2026 |
| Knowledge cutoff | — | — | — |
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
- GLM-5.1$22.80
- Qwen3.5 397B-A17B$13.20
Which should you choose?
Which is better: GLM-5, GLM-5.1 or Qwen3.5 397B-A17B?
Qwen3.5 397B-A17B is the better all-round choice, scoring 65/100 against GLM-5.1 (57) and GLM-5 (55). It leads on price, inputs & features and context window. GLM-5.1 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5, GLM-5.1 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); GLM-5.1 costs $1.40 input / $4.40 output per million tokens (official Z.AI 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 $2.15 for GLM-5.1 (1.6× as much).
Which scores higher on benchmarks?
GLM-5.1 scores higher on the Capabilities Index (ECI): GLM-5.1 149.9 (#51 of 148), Qwen3.5 397B-A17B 146.7 (#67 of 148) and GLM-5 145.8 (#74 of 148). The confidence ranges of the top two overlap (148.0–151.6 vs 144.8–148.2), so treat the gap as small. On individual benchmarks: GPQA Diamond — GLM-5.1 89.9%, GLM-5 87.8%, Qwen3.5 397B-A17B 86.4%; OTIS Mock AIME 2024–2025 — GLM-5.1 93.3%, Qwen3.5 397B-A17B 88.9%, 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, GLM-5.1 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?
Qwen3.5 397B-A17B has the largest context window at 262,144 tokens, against 204,800 for GLM-5 and 200,000 for GLM-5.1. Maximum output per response: GLM-5 up to 131,072, GLM-5.1 up to 131,072, Qwen3.5 397B-A17B up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GLM-5 accepts text; GLM-5.1 accepts text; Qwen3.5 397B-A17B accepts text, images, audio and video. Qwen3.5 397B-A17B handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
GLM-5.1 is the newest, released Apr 7, 2026. Qwen3.5 397B-A17B came out Feb 15, 2026; GLM-5 came out Feb 12, 2026.
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.
