GLM-5V-Turbo vs Laguna XS.2 vs Gemini Robotics-ER 1.6 Preview
Gemini Robotics-ER 1.6 Preview comes out ahead, 64 to 61 and 36 on our weighted score, though GLM-5V-Turbo is 5% cheaper per token.
Z.ai (Zhipu)GLM-5V-Turbo
61/100- ECI—
- Price$1.20 / $4.00
- Context200K
PoolsideLaguna XS.2
36/100- ECI—
- Price—
- Context262K
- Our pick
GoogleGemini Robotics-ER 1.6 Preview
64/100- ECI—
- Price$1.00 / $5.00
- Context131K
Gemini Robotics-ER 1.6 Preview is our pick
Gemini Robotics-ER 1.6 Preview is the better all-round choice, scoring 64/100 against GLM-5V-Turbo (61) and Laguna XS.2 (36). It leads on inputs & features. Laguna XS.2 wins on context window. The score weighs inputs & features 60%, context window 40%. 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-5V-TurboGLM-5V-Turbo $1.90 · Gemini Robotics-ER 1.6 Preview $2.00 per 1M tokens (3:1 blend) · Laguna XS.2 unpriced
- Longest contextLaguna XS.2Laguna XS.2 262,144 · GLM-5V-Turbo 200,000 · 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 · Laguna XS.2: Text · Gemini Robotics-ER 1.6 Preview: Text, Images, Audio, Video
- Self-hostingLaguna XS.2Publishes downloadable weights
| Measure | Weight | GLM-5V-Turbo | Laguna XS.2 | Gemini Robotics-ER 1.6 Preview |
|---|---|---|---|---|
| Inputs & features | 60% | 80 | 35 | 90 |
| Context window | 40% | 32 | 37 | 24 |
| Overall | 100% | 61/100 | 36/100 | 64/100 |
Left out because at least one model lacks the data: capability and price. 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) | Laguna XS.2Poolside | Gemini Robotics-ER 1.6 PreviewGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $1.20 | — | $1.00 (best) |
| Output | $4.00 (best) | — | $5.00 |
| Cached input | $0.24 | — | — |
| Blended (3:1) | $1.90 (best) | — | $2.00 |
| Long-context rate | Same rate | — | Same rate |
| Price source | Official Z.AI API | — | Median of 1 providers |
| Limits | |||
| Context window | 200,000 tokens | 262,144 tokens (best) | 131,072 tokens |
| Max output | 131,072 tokens (best) | 32,768 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 | Apr 28, 2026 | Apr 14, 2026 |
| Knowledge cutoff | — | — | 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
- Laguna XS.2—
- Gemini Robotics-ER 1.6 Preview$20.00
Which should you choose?
Which is better: GLM-5V-Turbo, Laguna XS.2 or Gemini Robotics-ER 1.6 Preview?
Gemini Robotics-ER 1.6 Preview is the better all-round choice, scoring 64/100 against GLM-5V-Turbo (61) and Laguna XS.2 (36). It leads on inputs & features. Laguna XS.2 wins on context window. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, GLM-5V-Turbo, Laguna XS.2 or Gemini Robotics-ER 1.6 Preview?
GLM-5V-Turbo is cheaper at $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 $1.90 per million tokens for GLM-5V-Turbo versus $2.00 for Gemini Robotics-ER 1.6 Preview (1.1× as much). Laguna XS.2 has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-5V-Turbo has not been scored yet, Laguna XS.2 has not been scored yet 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, Laguna XS.2 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?
Laguna XS.2 has the largest context window at 262,144 tokens, against 200,000 for GLM-5V-Turbo and 131,072 for Gemini Robotics-ER 1.6 Preview. Maximum output per response: GLM-5V-Turbo up to 131,072, Laguna XS.2 up to 32,768, 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; Laguna XS.2 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?
Laguna XS.2 publishes its weights and can be self-hosted; GLM-5V-Turbo and Gemini Robotics-ER 1.6 Preview is proprietary.
Which is newer?
Laguna XS.2 is the newest, released Apr 28, 2026. Gemini Robotics-ER 1.6 Preview came out Apr 14, 2026; GLM-5V-Turbo came out Apr 1, 2026. Knowledge cutoff: 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.
