Hermes 4 405B vs GLM-4.5V vs Qwen3 VL 235B A22B Thinking
GLM-4.5V comes out ahead, 48 to 44 and 35 on our weighted score, and it is the cheaper option too.
Nous ResearchHermes 4 405B
35/100- ECI—
- Price$1.00 / $3.00
- Context131K
- Our pick
Z.ai (Zhipu)GLM-4.5V
48/100- ECI—
- Price$0.60 / $1.80
- Context64K
Alibaba (Qwen)Qwen3 VL 235B A22B Thinking
44/100- ECI—
- Price$0.50 / $3.95
- Context131K
GLM-4.5V is our pick
GLM-4.5V is the better all-round choice, scoring 48/100 against Qwen3 VL 235B A22B Thinking (44) and Hermes 4 405B (35). It leads on price and 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 priceGLM-4.5VGLM-4.5V $0.90 · Qwen3 VL 235B A22B Thinking $1.36 · Hermes 4 405B $1.50 per 1M tokens (3:1 blend)
- Longest contextHermes 4 405B and Qwen3 VL 235B A22B ThinkingHermes 4 405B 131,072 · Qwen3 VL 235B A22B Thinking 131,072 · GLM-4.5V 64,000 tokens
- Widest inputsGLM-4.5VHermes 4 405B: Text · GLM-4.5V: Text, Images, Video · Qwen3 VL 235B A22B Thinking: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Hermes 4 405B | GLM-4.5V | Qwen3 VL 235B A22B Thinking |
|---|---|---|---|---|
| Price | 50% | 40 | 50 | 42 |
| Inputs & features | 30% | 35 | 70 | 60 |
| Context window | 20% | 25 | 12 | 25 |
| Overall | 100% | 35/100 | 48/100 | 44/100 |
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 | Hermes 4 405BNous Research | GLM-4.5VZ.ai (Zhipu) | Qwen3 VL 235B A22B ThinkingAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $1.00 | $0.60 | $0.50 (best) |
| Output | $3.00 | $1.80 (best) | $3.95 |
| Cached input | — | $0.11 | — |
| Blended (3:1) | $1.50 | $0.90 (best) | $1.36 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 5 providers | Official Z.AI API | Median of 5 providers |
| Limits | |||
| Context window | 131,072 tokens (best) | 64,000 tokens | 131,072 tokens (best) |
| Max output | 117,964 tokens (best) | 16,384 tokens | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | — | glm-4.5v | — |
| API providers | 5 | 17 (best) | 5 |
| Released | Aug 26, 2025 | Aug 11, 2025 | Sep 23, 2025 |
| Knowledge cutoff | Aug 31, 2024 | Apr 2025 | Mar 31, 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Hermes 4 405B$16.00
- GLM-4.5V$9.60
- Qwen3 VL 235B A22B Thinking$12.90
Which should you choose?
Which is better: Hermes 4 405B, GLM-4.5V or Qwen3 VL 235B A22B Thinking?
GLM-4.5V is the better all-round choice, scoring 48/100 against Qwen3 VL 235B A22B Thinking (44) and Hermes 4 405B (35). It leads on price and 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, Hermes 4 405B, GLM-4.5V or Qwen3 VL 235B A22B Thinking?
GLM-4.5V is cheaper at $0.60 input / $1.80 output per million tokens (official Z.AI API price). Qwen3 VL 235B A22B Thinking costs $0.50 input / $3.95 output per million tokens (median across 5 API providers); Hermes 4 405B costs $1.00 input / $3.00 output per million tokens (median across 5 API providers). At a typical mix of three input tokens to one output token, that is $0.90 per million tokens for GLM-4.5V versus $1.36 for Qwen3 VL 235B A22B Thinking (1.5× as much) and $1.50 for Hermes 4 405B (1.7× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Hermes 4 405B has not been scored yet, GLM-4.5V has not been scored yet and Qwen3 VL 235B A22B Thinking has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Hermes 4 405B, GLM-4.5V and Qwen3 VL 235B A22B Thinking 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?
Hermes 4 405B and Qwen3 VL 235B A22B Thinking have the largest context windows (131,072 and 131,072 tokens), against 64,000 for GLM-4.5V. Maximum output per response: Hermes 4 405B up to 117,964, GLM-4.5V up to 16,384, Qwen3 VL 235B A22B Thinking up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Hermes 4 405B accepts text; GLM-4.5V accepts text, images and video; Qwen3 VL 235B A22B Thinking accepts text and images. GLM-4.5V 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 VL 235B A22B Thinking is the newest, released Sep 23, 2025. Hermes 4 405B came out Aug 26, 2025; GLM-4.5V came out Aug 11, 2025. Knowledge cutoff: Hermes 4 405B Aug 31, 2024, GLM-4.5V Apr 2025, Qwen3 VL 235B A22B Thinking Mar 31, 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.
