GLM-5 vs Laguna XS 2.1 vs Nemotron 3.5 Lightning 30B A3B
Nemotron 3.5 Lightning 30B A3B comes out ahead, 71 to 68 and 37 on our weighted score, though Laguna XS 2.1 is 14% cheaper per token.
Z.ai (Zhipu)GLM-5
37/100- ECI145.8
- Price$1.00 / $3.20
- Context205K
PoolsideLaguna XS 2.1
68/100- ECI—
- Price$0.06 / $0.12
- Context262K
- Our pick
NVIDIANemotron 3.5 Lightning 30B A3B
71/100- ECI—
- Price$0.05 / $0.20
- Context262K
Nemotron 3.5 Lightning 30B A3B is our pick
Nemotron 3.5 Lightning 30B A3B is the better all-round choice, scoring 71/100 against Laguna XS 2.1 (68) and GLM-5 (37). It leads on 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 priceLaguna XS 2.1Laguna XS 2.1 $0.075 · Nemotron 3.5 Lightning 30B A3B $0.087 · GLM-5 $1.55 per 1M tokens (3:1 blend)
- Longest contextLaguna XS 2.1 and Nemotron 3.5 Lightning 30B A3BLaguna XS 2.1 262,144 · Nemotron 3.5 Lightning 30B A3B 262,144 · GLM-5 204,800 tokens
- Widest inputsSame inputsGLM-5: Text · Laguna XS 2.1: Text · Nemotron 3.5 Lightning 30B A3B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-5 | Laguna XS 2.1 | Nemotron 3.5 Lightning 30B A3B |
|---|---|---|---|---|
| Price | 50% | 41 | 100 | 100 |
| Inputs & features | 30% | 35 | 35 | 45 |
| Context window | 20% | 32 | 37 | 37 |
| Overall | 100% | 37/100 | 68/100 | 71/100 |
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-5Z.ai (Zhipu) | Laguna XS 2.1Poolside | Nemotron 3.5 Lightning 30B A3BNVIDIA |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.8 | — | — |
| ECI rank | #74 of 148 | — | — |
| GPQA DiamondGraduate-level science questions | 87.8% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 80.0% | — | — |
| SWE-bench VerifiedFixing real GitHub issues | 72.1% | — | — |
| Price per million tokens | |||
| Input | $1.00 | $0.06 | $0.05 (best) |
| Output | $3.20 | $0.12 (best) | $0.20 |
| Cached input | $0.20 | — | — |
| Blended (3:1) | $1.55 | $0.075 (best) | $0.087 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 1 providers | Median of 9 providers |
| Limits | |||
| Context window | 204,800 tokens | 262,144 tokens (best) | 262,144 tokens (best) |
| Max output | 131,072 tokens | 32,768 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-5 | poolside/laguna-xs-2.1 | nvidia/nemotron-3.5-lightning-30b-a3b |
| API providers | 27 (best) | 4 | 12 |
| Released | Feb 12, 2026 | Jul 2, 2026 | Aug 11, 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-5$16.40
- Laguna XS 2.1$0.84
- Nemotron 3.5 Lightning 30B A3B$0.90
Which should you choose?
Which is better: GLM-5, Laguna XS 2.1 or Nemotron 3.5 Lightning 30B A3B?
Nemotron 3.5 Lightning 30B A3B is the better all-round choice, scoring 71/100 against Laguna XS 2.1 (68) and GLM-5 (37). It leads on 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, GLM-5, Laguna XS 2.1 or Nemotron 3.5 Lightning 30B A3B?
Laguna XS 2.1 is cheaper at $0.06 input / $0.12 output per million tokens (median across 1 API provider; free on Poolside). Nemotron 3.5 Lightning 30B A3B costs $0.05 input / $0.20 output per million tokens (median across 9 API providers; free on Nvidia); GLM-5 costs $1.00 input / $3.20 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.075 per million tokens for Laguna XS 2.1 versus $0.087 for Nemotron 3.5 Lightning 30B A3B (1.2× as much) and $1.55 for GLM-5 (21× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-5 has an ECI of 145.8, Laguna XS 2.1 has not been scored yet and Nemotron 3.5 Lightning 30B A3B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Laguna XS 2.1 and Nemotron 3.5 Lightning 30B A3B 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 2.1 and Nemotron 3.5 Lightning 30B A3B have the largest context windows (262,144 and 262,144 tokens), against 204,800 for GLM-5. Maximum output per response: GLM-5 up to 131,072, Laguna XS 2.1 up to 32,768, Nemotron 3.5 Lightning 30B A3B up to 262,144 tokens.
Which can read images, PDFs, audio or video?
GLM-5 accepts text; Laguna XS 2.1 accepts text; Nemotron 3.5 Lightning 30B A3B accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Nemotron 3.5 Lightning 30B A3B is the newest, released Aug 11, 2026. Laguna XS 2.1 came out Jul 2, 2026; GLM-5 came out Feb 12, 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.
