GLM-4.5V vs Qwen Flash vs Qwen3-Next 80B-A3B Instruct
Qwen Flash comes out ahead, 68 to 49 and 39 on our weighted score, and it is the cheaper option too.
Z.ai (Zhipu)GLM-4.5V
49/100- ECI—
- Price$0.60 / $1.80
- Context64K
- Our pick
Alibaba (Qwen)Qwen Flash
68/100- ECI—
- Price$0.05 / $0.40
- Context1M
- Alibaba (Qwen)
Qwen3-Next 80B-A3B Instruct
39/100- ECI—
- Price$0.50 / $2.00
- Context131K
Qwen Flash is our pick
Qwen Flash is the better all-round choice, scoring 68/100 against GLM-4.5V (49) and Qwen3-Next 80B-A3B Instruct (39). It leads on price and context window. GLM-4.5V wins on inputs & features. 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 priceQwen FlashQwen Flash $0.138 · Qwen3-Next 80B-A3B Instruct $0.875 · GLM-4.5V $0.90 per 1M tokens (3:1 blend)
- Longest contextQwen FlashQwen Flash 1,000,000 · Qwen3-Next 80B-A3B Instruct 131,072 · GLM-4.5V 64,000 tokens
- Widest inputsGLM-4.5VGLM-4.5V: Text, Images, Video · Qwen Flash: Text · Qwen3-Next 80B-A3B Instruct: Text
- Self-hostingGLM-4.5V and Qwen3-Next 80B-A3B InstructPublishes downloadable weights
| Measure | Weight | GLM-4.5V | Qwen Flash | Qwen3-Next 80B-A3B Instruct |
|---|---|---|---|---|
| Price | 50% | 52 | 91 | 53 |
| Inputs & features | 30% | 70 | 35 | 25 |
| Context window | 20% | 12 | 60 | 24 |
| Overall | 100% | 49/100 | 68/100 | 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-4.5VZ.ai (Zhipu) | Qwen FlashAlibaba (Qwen) | Qwen3-Next 80B-A3B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.60 | $0.05 (best) | $0.50 |
| Output | $1.80 | $0.40 (best) | $2.00 |
| Cached input | — | — | — |
| Blended (3:1) | $0.90 | $0.138 (best) | $0.875 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Alibaba API | Official Alibaba API |
| Limits | |||
| Context window | 64,000 tokens | 1,000,000 tokens (best) | 131,072 tokens |
| Max output | 16,384 tokens | 32,768 tokens (best) | 32,768 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | glm-4.5v | qwen-flash | qwen3-next-80b-a3b-instruct |
| API providers | 11 | 6 | 13 (best) |
| Released | Aug 11, 2025 | Jul 28, 2025 | Sep 2025 |
| Knowledge cutoff | Apr 2025 | Apr 2024 | 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-4.5V$9.60
- Qwen Flash$1.30
- Qwen3-Next 80B-A3B Instruct$9.00
Which should you choose?
Which is better: GLM-4.5V, Qwen Flash or Qwen3-Next 80B-A3B Instruct?
Qwen Flash is the better all-round choice, scoring 68/100 against GLM-4.5V (49) and Qwen3-Next 80B-A3B Instruct (39). It leads on price and context window. GLM-4.5V wins on inputs & features. 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-4.5V, Qwen Flash or Qwen3-Next 80B-A3B Instruct?
Qwen Flash is cheaper at $0.05 input / $0.40 output per million tokens (official Alibaba API price). Qwen3-Next 80B-A3B Instruct costs $0.50 input / $2.00 output per million tokens (official Alibaba API price); GLM-4.5V costs $0.60 input / $1.80 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.138 per million tokens for Qwen Flash versus $0.875 for Qwen3-Next 80B-A3B Instruct (6.4× as much) and $0.90 for GLM-4.5V (6.5× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.5V has not been scored yet, Qwen Flash 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-4.5V, Qwen Flash 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. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen Flash has the largest context window at 1,000,000 tokens, against 131,072 for Qwen3-Next 80B-A3B Instruct and 64,000 for GLM-4.5V. Maximum output per response: GLM-4.5V up to 16,384, Qwen Flash up to 32,768, Qwen3-Next 80B-A3B Instruct up to 32,768 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5V accepts text, images and video; Qwen Flash accepts text; Qwen3-Next 80B-A3B Instruct accepts text. GLM-4.5V handles the widest range of inputs.
Are any of these open source?
GLM-4.5V and Qwen3-Next 80B-A3B Instruct publishes its weights and can be self-hosted; Qwen Flash is proprietary.
Which is newer?
Qwen3-Next 80B-A3B Instruct is the newest, released Sep 2025. GLM-4.5V came out Aug 11, 2025; Qwen Flash came out Jul 28, 2025. Knowledge cutoff: GLM-4.5V Apr 2025, Qwen Flash Apr 2024, 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.
