GLM-4.5V vs Qwen3-Next 80B-A3B Instruct vs Relace Apply 3
GLM-4.5V comes out ahead, 48 to 40 and 32 on our weighted score.
- Our pick
Z.ai (Zhipu)GLM-4.5V
48/100- ECI—
- Price$0.60 / $1.80
- Context64K
Alibaba (Qwen)Qwen3-Next 80B-A3B Instruct
40/100- ECI—
- Price$0.50 / $2.00
- Context262K
Relace
Relace Apply 3
32/100- ECI—
- Price$0.85 / $1.25
- Context256K
GLM-4.5V is our pick
GLM-4.5V is the better all-round choice, scoring 48/100 against Qwen3-Next 80B-A3B Instruct (40) and Relace Apply 3 (32). It leads 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 priceQwen3-Next 80B-A3B InstructQwen3-Next 80B-A3B Instruct $0.875 · GLM-4.5V $0.90 · Relace Apply 3 $0.95 per 1M tokens (3:1 blend)
- Longest contextQwen3-Next 80B-A3B InstructQwen3-Next 80B-A3B Instruct 262,144 · Relace Apply 3 256,000 · GLM-4.5V 64,000 tokens
- Widest inputsGLM-4.5VGLM-4.5V: Text, Images, Video · Qwen3-Next 80B-A3B Instruct: Text · Relace Apply 3: Text
- Self-hostingGLM-4.5V and Qwen3-Next 80B-A3B InstructPublishes downloadable weights
| Measure | Weight | GLM-4.5V | Qwen3-Next 80B-A3B Instruct | Relace Apply 3 |
|---|---|---|---|---|
| Price | 50% | 50 | 50 | 49 |
| Inputs & features | 30% | 70 | 25 | 0 |
| Context window | 20% | 12 | 37 | 36 |
| Overall | 100% | 48/100 | 40/100 | 32/100 |
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) | Qwen3-Next 80B-A3B InstructAlibaba (Qwen) | Relace Apply 3Relace |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.60 | $0.50 (best) | $0.85 |
| Output | $1.80 | $2.00 | $1.25 (best) |
| Cached input | $0.11 | — | — |
| Blended (3:1) | $0.90 | $0.875 (best) | $0.95 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Alibaba API | Median of 3 providers |
| Limits | |||
| Context window | 64,000 tokens | 262,144 tokens (best) | 256,000 tokens |
| Max output | 16,384 tokens | 32,768 tokens | 128,000 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 | No | No |
| Tool calling | Yes | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | glm-4.5v | qwen3-next-80b-a3b-instruct | — |
| API providers | 17 | 18 (best) | 3 |
| Released | Aug 11, 2025 | Sep 2025 | Sep 26, 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-4.5V$9.60
- Qwen3-Next 80B-A3B Instruct$9.00
- Relace Apply 3$11.00
Which should you choose?
Which is better: GLM-4.5V, Qwen3-Next 80B-A3B Instruct or Relace Apply 3?
GLM-4.5V is the better all-round choice, scoring 48/100 against Qwen3-Next 80B-A3B Instruct (40) and Relace Apply 3 (32). It leads 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, Qwen3-Next 80B-A3B Instruct or Relace Apply 3?
Qwen3-Next 80B-A3B Instruct is cheaper at $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); Relace Apply 3 costs $0.85 input / $1.25 output per million tokens (median across 3 API providers). At a typical mix of three input tokens to one output token, that is $0.875 per million tokens for Qwen3-Next 80B-A3B Instruct versus $0.90 for GLM-4.5V (1× as much) and $0.95 for Relace Apply 3 (1.1× 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, Qwen3-Next 80B-A3B Instruct has not been scored yet and Relace Apply 3 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5V, Qwen3-Next 80B-A3B Instruct and Relace Apply 3 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Relace Apply 3 does not support tool calling, which most coding agents need.
Which has the bigger context window?
Qwen3-Next 80B-A3B Instruct has the largest context window at 262,144 tokens, against 256,000 for Relace Apply 3 and 64,000 for GLM-4.5V. Maximum output per response: GLM-4.5V up to 16,384, Qwen3-Next 80B-A3B Instruct up to 32,768, Relace Apply 3 up to 128,000 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5V accepts text, images and video; Qwen3-Next 80B-A3B Instruct accepts text; Relace Apply 3 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; Relace Apply 3 is proprietary.
Which is newer?
Relace Apply 3 is the newest, released Sep 26, 2025. Qwen3-Next 80B-A3B Instruct came out Sep 2025; GLM-4.5V came out Aug 11, 2025. Knowledge cutoff: GLM-4.5V 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 Nov 2, 2026.
