GLM-5V-Turbo vs Laguna XS.2 vs Nemotron 3 Nano Omni 30B A3B Reasoning
Nemotron 3 Nano Omni 30B A3B Reasoning comes out ahead, 68 to 61 and 36 on our weighted score, and it is the cheaper option too.
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
NVIDIANemotron 3 Nano Omni 30B A3B Reasoning
68/100- ECI—
- Price$0.25 / $0.85
- Context256K
Nemotron 3 Nano Omni 30B A3B Reasoning is our pick
Nemotron 3 Nano Omni 30B A3B Reasoning is the better all-round choice, scoring 68/100 against GLM-5V-Turbo (61) and Laguna XS.2 (36). It leads on inputs & features. 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 priceNemotron 3 Nano Omni 30B A3B ReasoningNemotron 3 Nano Omni 30B A3B Reasoning $0.40 · GLM-5V-Turbo $1.90 per 1M tokens (3:1 blend) · Laguna XS.2 unpriced
- Longest contextLaguna XS.2Laguna XS.2 262,144 · Nemotron 3 Nano Omni 30B A3B Reasoning 256,000 · GLM-5V-Turbo 200,000 tokens
- Widest inputsGLM-5V-Turbo and Nemotron 3 Nano Omni 30B A3B ReasoningGLM-5V-Turbo: Text, Images, PDFs, Video · Laguna XS.2: Text · Nemotron 3 Nano Omni 30B A3B Reasoning: Text, Images, Audio, Video
- Self-hostingLaguna XS.2 and Nemotron 3 Nano Omni 30B A3B ReasoningPublishes downloadable weights
| Measure | Weight | GLM-5V-Turbo | Laguna XS.2 | Nemotron 3 Nano Omni 30B A3B Reasoning |
|---|---|---|---|---|
| Inputs & features | 60% | 80 | 35 | 90 |
| Context window | 40% | 32 | 37 | 36 |
| Overall | 100% | 61/100 | 36/100 | 68/100 |
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 | Nemotron 3 Nano Omni 30B A3B ReasoningNVIDIA |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $1.20 | — | $0.25 (best) |
| Output | $4.00 | — | $0.85 (best) |
| Cached input | $0.24 | — | — |
| Blended (3:1) | $1.90 | — | $0.40 (best) |
| Long-context rate | Same rate | — | Same rate |
| Price source | Official Z.AI API | — | Median of 4 providers |
| Limits | |||
| Context window | 200,000 tokens | 262,144 tokens (best) | 256,000 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 | Open |
| API model ID | glm-5v-turbo | — | nvidia/nemotron-3-nano-omni-30b-a3b-reasoning |
| API providers | 14 (best) | 1 | 8 |
| Released | Apr 1, 2026 | Apr 28, 2026 | Apr 28, 2026 |
| Knowledge cutoff | — | — | — |
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—
- Nemotron 3 Nano Omni 30B A3B Reasoning$4.20
Which should you choose?
Which is better: GLM-5V-Turbo, Laguna XS.2 or Nemotron 3 Nano Omni 30B A3B Reasoning?
Nemotron 3 Nano Omni 30B A3B Reasoning is the better all-round choice, scoring 68/100 against GLM-5V-Turbo (61) and Laguna XS.2 (36). It leads on inputs & features. 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 Nemotron 3 Nano Omni 30B A3B Reasoning?
Nemotron 3 Nano Omni 30B A3B Reasoning is cheaper at $0.25 input / $0.85 output per million tokens (median across 4 API providers; free on Nvidia). 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 $0.40 per million tokens for Nemotron 3 Nano Omni 30B A3B Reasoning versus $1.90 for GLM-5V-Turbo (4.7× 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 Nemotron 3 Nano Omni 30B A3B Reasoning 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 Nemotron 3 Nano Omni 30B A3B Reasoning 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 256,000 for Nemotron 3 Nano Omni 30B A3B Reasoning and 200,000 for GLM-5V-Turbo. Maximum output per response: GLM-5V-Turbo up to 131,072, Laguna XS.2 up to 32,768, Nemotron 3 Nano Omni 30B A3B Reasoning 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; Nemotron 3 Nano Omni 30B A3B Reasoning accepts text, images, audio and video. GLM-5V-Turbo handles the widest range of inputs.
Are any of these open source?
Laguna XS.2 and Nemotron 3 Nano Omni 30B A3B Reasoning publishes its weights and can be self-hosted; GLM-5V-Turbo is proprietary.
Which is newer?
Laguna XS.2 is the newest, released Apr 28, 2026. Nemotron 3 Nano Omni 30B A3B Reasoning came out Apr 28, 2026; GLM-5V-Turbo came out Apr 1, 2026.
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.
