GLM-5 vs Qwen3.5 397B-A17B vs Qwen3 Coder Next
Qwen3.5 397B-A17B comes out ahead, 56 to 51 and 37 on our weighted score, though Qwen3 Coder Next is 3× cheaper per token.
Z.ai (Zhipu)GLM-5
37/100- ECI145.8
- Price$1.00 / $3.20
- Context205K
- Our pick
Alibaba (Qwen)Qwen3.5 397B-A17B
56/100- ECI146.7
- Price$0.60 / $3.60
- Context262K
- Alibaba (Qwen)
Qwen3 Coder Next
51/100- ECI—
- Price$0.20 / $1.20
- Context262K
Qwen3.5 397B-A17B is our pick
Qwen3.5 397B-A17B is the better all-round choice, scoring 56/100 against Qwen3 Coder Next (51) and GLM-5 (37). It leads on inputs & features. Qwen3 Coder Next 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 priceQwen3 Coder NextQwen3 Coder Next $0.45 · Qwen3.5 397B-A17B $1.35 · GLM-5 $1.55 per 1M tokens (3:1 blend)
- Longest contextQwen3.5 397B-A17B and Qwen3 Coder NextQwen3.5 397B-A17B 262,144 · Qwen3 Coder Next 262,144 · GLM-5 204,800 tokens
- Widest inputsQwen3.5 397B-A17BGLM-5: Text · Qwen3.5 397B-A17B: Text, Images, Audio, Video · Qwen3 Coder Next: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-5 | Qwen3.5 397B-A17B | Qwen3 Coder Next |
|---|---|---|---|---|
| Price | 50% | 41 | 44 | 66 |
| Inputs & features | 30% | 35 | 90 | 35 |
| Context window | 20% | 32 | 37 | 37 |
| Overall | 100% | 37/100 | 56/100 | 51/100 |
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 | GLM-5Z.ai (Zhipu) | Qwen3.5 397B-A17BAlibaba (Qwen) | Qwen3 Coder NextAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.8 | 146.7 (best) | — |
| ECI rank | #74 of 148 | #67 of 148 (best) | — |
| GPQA DiamondGraduate-level science questions | 87.8% (best) | 86.4% | — |
| FrontierMath Tiers 1–3Research-level mathematics | — | 31.2% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 80.0% | 88.9% (best) | — |
| SWE-bench VerifiedFixing real GitHub issues | 72.1% | — | — |
| Price per million tokens | |||
| Input | $1.00 | $0.60 | $0.20 (best) |
| Output | $3.20 | $3.60 | $1.20 (best) |
| Cached input | $0.20 | — | — |
| Blended (3:1) | $1.55 | $1.35 | $0.45 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Alibaba API | Median of 11 providers |
| Limits | |||
| Context window | 204,800 tokens | 262,144 tokens (best) | 262,144 tokens (best) |
| Max output | 131,072 tokens (best) | 65,536 tokens | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | Yes | No |
| Video | No | Yes | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-5 | qwen3.5-397b-a17b | — |
| API providers | 27 (best) | 23 | 11 |
| Released | Feb 12, 2026 | Feb 15, 2026 | Feb 3, 2026 |
| Knowledge cutoff | — | — | Sep 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
- Qwen3.5 397B-A17B$13.20
- Qwen3 Coder Next$4.40
Which should you choose?
Which is better: GLM-5, Qwen3.5 397B-A17B or Qwen3 Coder Next?
Qwen3.5 397B-A17B is the better all-round choice, scoring 56/100 against Qwen3 Coder Next (51) and GLM-5 (37). It leads on inputs & features. Qwen3 Coder Next 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, GLM-5, Qwen3.5 397B-A17B or Qwen3 Coder Next?
Qwen3 Coder Next is cheaper at $0.20 input / $1.20 output per million tokens (median across 11 API providers). Qwen3.5 397B-A17B costs $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). At a typical mix of three input tokens to one output token, that is $0.45 per million tokens for Qwen3 Coder Next versus $1.35 for Qwen3.5 397B-A17B (3× as much) and $1.55 for GLM-5 (3.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-5 has an ECI of 145.8, Qwen3.5 397B-A17B has an ECI of 146.7 and Qwen3 Coder Next has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3.5 397B-A17B and Qwen3 Coder Next 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?
Qwen3.5 397B-A17B and Qwen3 Coder Next have the largest context windows (262,144 and 262,144 tokens), against 204,800 for GLM-5. Maximum output per response: GLM-5 up to 131,072, Qwen3.5 397B-A17B up to 65,536, Qwen3 Coder Next up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GLM-5 accepts text; Qwen3.5 397B-A17B accepts text, images, audio and video; Qwen3 Coder Next accepts text. 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?
Qwen3.5 397B-A17B is the newest, released Feb 15, 2026. GLM-5 came out Feb 12, 2026; Qwen3 Coder Next came out Feb 3, 2026. Knowledge cutoff: Qwen3 Coder Next Sep 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.
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