GLM-4.5V vs Palmyra X5 vs Qwen3-Next 80B-A3B Instruct
Too close to call on our weighted score (GLM-4.5V 49, Palmyra X5 48, Qwen3-Next 80B-A3B Instruct 39). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-4.5V
49/100- ECI—
- Price$0.60 / $1.80
- Context64K
Writer
Palmyra X5
48/100- ECI—
- Price$0.60 / $6.00
- Context1M
Alibaba (Qwen)Qwen3-Next 80B-A3B Instruct
39/100- ECI—
- Price$0.50 / $2.00
- Context131K
Too close to call
It is close. Our weighted score puts them within 1 points (GLM-4.5V 49/100, Palmyra X5 48/100, Qwen3-Next 80B-A3B Instruct 39/100), so choose by what matters most for your work: Qwen3-Next 80B-A3B Instruct on price and Palmyra X5 for long inputs. 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 · Palmyra X5 $1.95 per 1M tokens (3:1 blend)
- Longest contextPalmyra X5Palmyra X5 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 · Palmyra X5: Text, Images · Qwen3-Next 80B-A3B Instruct: Text
- Self-hostingGLM-4.5V and Qwen3-Next 80B-A3B InstructPublishes downloadable weights
| Measure | Weight | GLM-4.5V | Palmyra X5 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|---|---|
| Price | 50% | 52 | 36 | 53 |
| Inputs & features | 30% | 70 | 60 | 25 |
| Context window | 20% | 12 | 60 | 24 |
| Overall | 100% | 49/100 | 48/100 | 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) | Palmyra X5Writer | Qwen3-Next 80B-A3B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.60 | $0.60 | $0.50 (best) |
| Output | $1.80 (best) | $6.00 | $2.00 |
| Cached input | — | — | — |
| Blended (3:1) | $0.90 | $1.95 | $0.875 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 1 providers | Official Alibaba API |
| Limits | |||
| Context window | 64,000 tokens | 1,000,000 tokens (best) | 131,072 tokens |
| Max output | 16,384 tokens | 8,192 tokens | 32,768 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | 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 | — | qwen3-next-80b-a3b-instruct |
| API providers | 11 | 1 | 13 (best) |
| Released | Aug 11, 2025 | Apr 28, 2025 | 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-4.5V$9.60
- Palmyra X5$18.00
- Qwen3-Next 80B-A3B Instruct$9.00
Which should you choose?
Which is better: GLM-4.5V, Palmyra X5 or Qwen3-Next 80B-A3B Instruct?
It is close. Our weighted score puts them within 1 points (GLM-4.5V 49/100, Palmyra X5 48/100, Qwen3-Next 80B-A3B Instruct 39/100), so choose by what matters most for your work: Qwen3-Next 80B-A3B Instruct on price and Palmyra X5 for long inputs. 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, Palmyra X5 or Qwen3-Next 80B-A3B Instruct?
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); Palmyra X5 costs $0.60 input / $6.00 output per million tokens (median across 1 API provider). 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 $1.95 for Palmyra X5 (2.2× 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, Palmyra X5 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, Palmyra X5 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?
Palmyra X5 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, Palmyra X5 up to 8,192, 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; Palmyra X5 accepts text and images; 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; Palmyra X5 is proprietary.
Which is newer?
Qwen3-Next 80B-A3B Instruct is the newest, released Sep 2025. GLM-4.5V came out Aug 11, 2025; Palmyra X5 came out Apr 28, 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 Oct 4, 2026.
