GLM-5V-Turbo vs QwQ 32B vs Gemini Robotics-ER 1.6 Preview
Too close to call on our weighted score (Gemini Robotics-ER 1.6 Preview 50, GLM-5V-Turbo 49, QwQ 32B 43). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-5V-Turbo
49/100- ECI—
- Price$1.20 / $4.00
- Context200K
Alibaba (Qwen)QwQ 32B
43/100- ECI137.6
- Price$0.66 / $1.00
- Context131K
GoogleGemini Robotics-ER 1.6 Preview
50/100- ECI—
- Price$1.00 / $5.00
- Context131K
Too close to call
It is close. Our weighted score puts them within 1 points (Gemini Robotics-ER 1.6 Preview 50/100, GLM-5V-Turbo 49/100, QwQ 32B 43/100), so choose by what matters most for your work: QwQ 32B 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 priceQwQ 32BQwQ 32B $0.745 · GLM-5V-Turbo $1.90 · Gemini Robotics-ER 1.6 Preview $2.00 per 1M tokens (3:1 blend)
- Longest contextGLM-5V-TurboGLM-5V-Turbo 200,000 · QwQ 32B 131,072 · Gemini Robotics-ER 1.6 Preview 131,072 tokens
- Widest inputsGLM-5V-Turbo and Gemini Robotics-ER 1.6 PreviewGLM-5V-Turbo: Text, Images, PDFs, Video · QwQ 32B: Text · Gemini Robotics-ER 1.6 Preview: Text, Images, Audio, Video
- Self-hostingQwQ 32BPublishes downloadable weights
| Measure | Weight | GLM-5V-Turbo | QwQ 32B | Gemini Robotics-ER 1.6 Preview |
|---|---|---|---|---|
| Price | 50% | 37 | 56 | 36 |
| Inputs & features | 30% | 80 | 35 | 90 |
| Context window | 20% | 32 | 24 | 24 |
| Overall | 100% | 49/100 | 43/100 | 50/100 |
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-5V-TurboZ.ai (Zhipu) | QwQ 32BAlibaba (Qwen) | Gemini Robotics-ER 1.6 PreviewGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 137.6 | — |
| ECI rank | — | #109 of 148 | — |
| GPQA DiamondGraduate-level science questions | — | 65.3% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 59.2% | — |
| Price per million tokens | |||
| Input | $1.20 | $0.66 (best) | $1.00 |
| Output | $4.00 | $1.00 (best) | $5.00 |
| Cached input | $0.24 | — | — |
| Blended (3:1) | $1.90 | $0.745 (best) | $2.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 1 providers | Median of 1 providers |
| Limits | |||
| Context window | 200,000 tokens (best) | 131,072 tokens | 131,072 tokens |
| Max output | 131,072 tokens (best) | 8,192 tokens | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | Yes | No | No |
| Audio | No | No | Yes |
| Video | Yes | No | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | glm-5v-turbo | — | — |
| API providers | 14 (best) | 1 | 1 |
| Released | Apr 1, 2026 | Mar 5, 2025 | Apr 14, 2026 |
| Knowledge cutoff | — | Apr 2024 | Jan 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-5V-Turbo$20.00
- QwQ 32B$8.60
- Gemini Robotics-ER 1.6 Preview$20.00
Which should you choose?
Which is better: GLM-5V-Turbo, QwQ 32B or Gemini Robotics-ER 1.6 Preview?
It is close. Our weighted score puts them within 1 points (Gemini Robotics-ER 1.6 Preview 50/100, GLM-5V-Turbo 49/100, QwQ 32B 43/100), so choose by what matters most for your work: QwQ 32B 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, GLM-5V-Turbo, QwQ 32B or Gemini Robotics-ER 1.6 Preview?
QwQ 32B is cheaper at $0.66 input / $1.00 output per million tokens (median across 1 API provider). GLM-5V-Turbo costs $1.20 input / $4.00 output per million tokens (official Z.AI API price); Gemini Robotics-ER 1.6 Preview costs $1.00 input / $5.00 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.745 per million tokens for QwQ 32B versus $1.90 for GLM-5V-Turbo (2.6× as much) and $2.00 for Gemini Robotics-ER 1.6 Preview (2.7× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-5V-Turbo has not been scored yet, QwQ 32B has an ECI of 137.6 and Gemini Robotics-ER 1.6 Preview has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-5V-Turbo, QwQ 32B and Gemini Robotics-ER 1.6 Preview 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 QwQ 32B and 131,072 for Gemini Robotics-ER 1.6 Preview. Maximum output per response: GLM-5V-Turbo up to 131,072, QwQ 32B up to 8,192, Gemini Robotics-ER 1.6 Preview up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GLM-5V-Turbo accepts text, images, PDFs and video; QwQ 32B accepts text; Gemini Robotics-ER 1.6 Preview accepts text, images, audio and video. GLM-5V-Turbo handles the widest range of inputs.
Are any of these open source?
QwQ 32B publishes its weights and can be self-hosted; GLM-5V-Turbo and Gemini Robotics-ER 1.6 Preview is proprietary.
Which is newer?
Gemini Robotics-ER 1.6 Preview is the newest, released Apr 14, 2026. GLM-5V-Turbo came out Apr 1, 2026; QwQ 32B came out Mar 5, 2025. Knowledge cutoff: QwQ 32B Apr 2024, Gemini Robotics-ER 1.6 Preview Jan 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.
