GLM-4.7-FlashX vs Qwen3-Next 80B-A3B Instruct
GLM-4.7-FlashX comes out ahead, 61 to 39 on our weighted score, and it is the cheaper option too.
- Our pick
Z.ai (Zhipu)GLM-4.7-FlashX
61/100- ECI—
- Price$0.07 / $0.40
- Context200K
Alibaba (Qwen)Qwen3-Next 80B-A3B Instruct
39/100- ECI—
- Price$0.50 / $2.00
- Context131K
Add a model
Make it a three-way comparison.
GLM-4.7-FlashX is our pick
GLM-4.7-FlashX is the better all-round choice, scoring 61/100 against Qwen3-Next 80B-A3B Instruct (39). It leads on price, inputs & features and context window. 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-4.7-FlashXGLM-4.7-FlashX $0.152 · Qwen3-Next 80B-A3B Instruct $0.875 per 1M tokens (3:1 blend)
- Longest contextGLM-4.7-FlashXGLM-4.7-FlashX 200,000 · Qwen3-Next 80B-A3B Instruct 131,072 tokens
- Widest inputsSame inputsGLM-4.7-FlashX: Text · Qwen3-Next 80B-A3B Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.7-FlashX | Qwen3-Next 80B-A3B Instruct |
|---|---|---|---|
| Price | 50% | 89 | 53 |
| Inputs & features | 30% | 35 | 25 |
| Context window | 20% | 32 | 24 |
| Overall | 100% | 61/100 | 39/100 |
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-4.7-FlashXZ.ai (Zhipu) | Qwen3-Next 80B-A3B InstructAlibaba (Qwen) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.07 (best) | $0.50 |
| Output | $0.40 (best) | $2.00 |
| Cached input | $0.01 | — |
| Blended (3:1) | $0.152 (best) | $0.875 |
| Long-context rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Alibaba API |
| Limits | ||
| Context window | 200,000 tokens (best) | 131,072 tokens |
| Max output | 131,072 tokens (best) | 32,768 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | No |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Open | Open |
| API model ID | glm-4.7-flashx | qwen3-next-80b-a3b-instruct |
| API providers | 8 | 13 (best) |
| Released | Jan 19, 2026 | Sep 2025 |
| Knowledge cutoff | Apr 2025 | Apr 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-4.7-FlashX$1.50
- Qwen3-Next 80B-A3B Instruct$9.00
Which should you choose?
Which is better: GLM-4.7-FlashX or Qwen3-Next 80B-A3B Instruct?
GLM-4.7-FlashX is the better all-round choice, scoring 61/100 against Qwen3-Next 80B-A3B Instruct (39). It leads on price, inputs & features and context window. 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-4.7-FlashX or Qwen3-Next 80B-A3B Instruct?
GLM-4.7-FlashX is cheaper at $0.07 input / $0.40 output per million tokens (official Z.AI API price). Qwen3-Next 80B-A3B Instruct costs $0.50 input / $2.00 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.152 per million tokens for GLM-4.7-FlashX versus $0.875 for Qwen3-Next 80B-A3B Instruct (5.7× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. GLM-4.7-FlashX has not been scored yet and Qwen3-Next 80B-A3B Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.7-FlashX and Qwen3-Next 80B-A3B Instruct yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.
Which has the bigger context window?
GLM-4.7-FlashX has the largest context window at 200,000 tokens, against 131,072 for Qwen3-Next 80B-A3B Instruct. Maximum output per response: GLM-4.7-FlashX up to 131,072, Qwen3-Next 80B-A3B Instruct up to 32,768 tokens.
Which can read images, PDFs, audio or video?
GLM-4.7-FlashX accepts text; Qwen3-Next 80B-A3B Instruct accepts text. They handle the same number of input types.
Are any of these open source?
Yes, both publish their weights, so you can self-host them.
Which is newer?
GLM-4.7-FlashX is the newest, released Jan 19, 2026. Qwen3-Next 80B-A3B Instruct came out Sep 2025. Knowledge cutoff: GLM-4.7-FlashX Apr 2025, Qwen3-Next 80B-A3B Instruct Apr 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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