GLM-4.5-Flash vs Laguna M.1 vs Nemotron VoiceChat
Too close to call on our weighted score (Laguna M.1 68, GLM-4.5-Flash 65, Nemotron VoiceChat 65). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-4.5-Flash
65/100- ECI—
- PriceFree / Free
- Context131K
PoolsideLaguna M.1
68/100- ECI—
- PriceFree / Free
- Context262K
NVIDIANemotron VoiceChat
65/100- ECI—
- PriceFree / Free
- Context128K
Too close to call
It is close. Our weighted score puts them within 2 points (Laguna M.1 68/100, GLM-4.5-Flash 65/100, Nemotron VoiceChat 65/100), so choose by what matters most for your work: GLM-4.5-Flash on price and Laguna M.1 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 priceSame priceGLM-4.5-Flash Free · Laguna M.1 Free · Nemotron VoiceChat Free per 1M tokens (3:1 blend)
- Longest contextLaguna M.1Laguna M.1 262,144 · GLM-4.5-Flash 131,072 · Nemotron VoiceChat 128,000 tokens
- Widest inputsNemotron VoiceChatGLM-4.5-Flash: Text · Laguna M.1: Text · Nemotron VoiceChat: Text, Audio
- Self-hostingLaguna M.1 and Nemotron VoiceChatPublishes downloadable weights
| Measure | Weight | GLM-4.5-Flash | Laguna M.1 | Nemotron VoiceChat |
|---|---|---|---|---|
| Price | 50% | 100 | 100 | 100 |
| Inputs & features | 30% | 35 | 35 | 35 |
| Context window | 20% | 24 | 37 | 24 |
| Overall | 100% | 65/100 | 68/100 | 65/100 |
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-4.5-FlashZ.ai (Zhipu) | Laguna M.1Poolside | Nemotron VoiceChatNVIDIA |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | Free | Free | Free |
| Output | Free | Free | Free |
| Cached input | — | — | — |
| Blended (3:1) | Free | Free | Free |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Poolside API | Official Nvidia API |
| Limits | |||
| Context window | 131,072 tokens | 262,144 tokens (best) | 128,000 tokens |
| Max output | 98,304 tokens (best) | 32,768 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | Yes |
| Video | No | No | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | glm-4.5-flash | poolside/laguna-m.1 | nvidia/nemotron-voicechat |
| API providers | 4 (best) | 2 | 1 |
| Released | Jul 28, 2025 | Apr 28, 2026 | Mar 16, 2026 |
| Knowledge cutoff | Apr 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-4.5-FlashFree
- Laguna M.1Free
- Nemotron VoiceChatFree
Which should you choose?
Which is better: GLM-4.5-Flash, Laguna M.1 or Nemotron VoiceChat?
It is close. Our weighted score puts them within 2 points (Laguna M.1 68/100, GLM-4.5-Flash 65/100, Nemotron VoiceChat 65/100), so choose by what matters most for your work: GLM-4.5-Flash on price and Laguna M.1 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-4.5-Flash, Laguna M.1 or Nemotron VoiceChat?
GLM-4.5-Flash, Laguna M.1 and Nemotron VoiceChat cost the same: Free input / Free output per million tokens.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.5-Flash has not been scored yet, Laguna M.1 has not been scored yet and Nemotron VoiceChat has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5-Flash, Laguna M.1 and Nemotron VoiceChat 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 M.1 has the largest context window at 262,144 tokens, against 131,072 for GLM-4.5-Flash and 128,000 for Nemotron VoiceChat. Maximum output per response: GLM-4.5-Flash up to 98,304, Laguna M.1 up to 32,768, Nemotron VoiceChat up to 8,192 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5-Flash accepts text; Laguna M.1 accepts text; Nemotron VoiceChat accepts text and audio. Nemotron VoiceChat handles the widest range of inputs.
Are any of these open source?
Laguna M.1 and Nemotron VoiceChat publishes its weights and can be self-hosted; GLM-4.5-Flash is proprietary.
Which is newer?
Laguna M.1 is the newest, released Apr 28, 2026. Nemotron VoiceChat came out Mar 16, 2026; GLM-4.5-Flash came out Jul 28, 2025. Knowledge cutoff: GLM-4.5-Flash Apr 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.
