Apertus 70B vs GLM-5V-Turbo vs Qwen3 VL 235B A22B Thinking
Too close to call on our weighted score (GLM-5V-Turbo 49, Qwen3 VL 235B A22B Thinking 48, Apertus 70B 33). The right pick depends on what you value most.
Swiss AI
Apertus 70B
33/100- ECI—
- Price$0.82 / $2.42
- Context66K
Z.ai (Zhipu)GLM-5V-Turbo
49/100- ECI—
- Price$1.20 / $4.00
- Context200K
Alibaba (Qwen)Qwen3 VL 235B A22B Thinking
48/100- ECI—
- Price$0.40 / $4.00
- Context131K
Too close to call
It is close. Our weighted score puts them within a point (GLM-5V-Turbo 49/100, Qwen3 VL 235B A22B Thinking 48/100, Apertus 70B 33/100), so choose by what matters most for your work: Apertus 70B on price and GLM-5V-Turbo 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 priceApertus 70BApertus 70B $1.22 · Qwen3 VL 235B A22B Thinking $1.30 · GLM-5V-Turbo $1.90 per 1M tokens (3:1 blend)
- Longest contextGLM-5V-TurboGLM-5V-Turbo 200,000 · Qwen3 VL 235B A22B Thinking 131,072 · Apertus 70B 65,536 tokens
- Widest inputsGLM-5V-TurboApertus 70B: Text · GLM-5V-Turbo: Text, Images, PDFs, Video · Qwen3 VL 235B A22B Thinking: Text, Images
- Self-hostingApertus 70B and Qwen3 VL 235B A22B ThinkingPublishes downloadable weights (Apache-2.0)
| Measure | Weight | Apertus 70B | GLM-5V-Turbo | Qwen3 VL 235B A22B Thinking |
|---|---|---|---|---|
| Price | 50% | 46 | 37 | 44 |
| Inputs & features | 30% | 25 | 80 | 70 |
| Context window | 20% | 12 | 32 | 24 |
| Overall | 100% | 33/100 | 49/100 | 48/100 |
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 | Apertus 70BSwiss AI | GLM-5V-TurboZ.ai (Zhipu) | Qwen3 VL 235B A22B ThinkingAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.82 | $1.20 | $0.40 (best) |
| Output | $2.42 (best) | $4.00 | $4.00 |
| Cached input | — | $0.24 | — |
| Blended (3:1) | $1.22 (best) | $1.90 | $1.30 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 3 providers | Official Z.AI API | Median of 9 providers |
| Limits | |||
| Context window | 65,536 tokens | 200,000 tokens (best) | 131,072 tokens |
| Max output | 8,192 tokens | 131,072 tokens (best) | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | No | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | OpenApache-2.0 | Proprietary | Open |
| API model ID | — | glm-5v-turbo | — |
| API providers | 3 | 14 (best) | 9 |
| Released | Sep 2, 2025 | Apr 1, 2026 | Sep 23, 2025 |
| Knowledge cutoff | Sep 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.
- Apertus 70B$13.04
- GLM-5V-Turbo$20.00
- Qwen3 VL 235B A22B Thinking$12.00
Which should you choose?
Which is better: Apertus 70B, GLM-5V-Turbo or Qwen3 VL 235B A22B Thinking?
It is close. Our weighted score puts them within a point (GLM-5V-Turbo 49/100, Qwen3 VL 235B A22B Thinking 48/100, Apertus 70B 33/100), so choose by what matters most for your work: Apertus 70B on price and GLM-5V-Turbo 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, Apertus 70B, GLM-5V-Turbo or Qwen3 VL 235B A22B Thinking?
Apertus 70B is cheaper at $0.82 input / $2.42 output per million tokens (median across 3 API providers). Qwen3 VL 235B A22B Thinking costs $0.40 input / $4.00 output per million tokens (median across 9 API providers); GLM-5V-Turbo costs $1.20 input / $4.00 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $1.22 per million tokens for Apertus 70B versus $1.30 for Qwen3 VL 235B A22B Thinking (1.1× as much) and $1.90 for GLM-5V-Turbo (1.6× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Apertus 70B has not been scored yet, GLM-5V-Turbo 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 Apertus 70B, GLM-5V-Turbo 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?
GLM-5V-Turbo has the largest context window at 200,000 tokens, against 131,072 for Qwen3 VL 235B A22B Thinking and 65,536 for Apertus 70B. Maximum output per response: Apertus 70B up to 8,192, GLM-5V-Turbo up to 131,072, Qwen3 VL 235B A22B Thinking up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Apertus 70B accepts text; GLM-5V-Turbo accepts text, images, PDFs and video; Qwen3 VL 235B A22B Thinking accepts text and images. GLM-5V-Turbo handles the widest range of inputs.
Are any of these open source?
Apertus 70B and Qwen3 VL 235B A22B Thinking publishes its weights (Apache-2.0) and can be self-hosted; GLM-5V-Turbo is proprietary.
Which is newer?
GLM-5V-Turbo is the newest, released Apr 1, 2026. Qwen3 VL 235B A22B Thinking came out Sep 23, 2025; Apertus 70B came out Sep 2, 2025. Knowledge cutoff: Apertus 70B Sep 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 Oct 4, 2026.
