Qwen3.6 27B vs Nemotron 3.5 Lightning 30B A3B vs GLM-5
Nemotron 3.5 Lightning 30B A3B comes out ahead, 71 to 56 and 37 on our weighted score, and it is the cheaper option too.
Alibaba (Qwen)Qwen3.6 27B
56/100- ECI146.5
- Price$0.60 / $3.60
- Context262K
- Our pick
NVIDIANemotron 3.5 Lightning 30B A3B
71/100- ECI—
- Price$0.05 / $0.20
- Context262K
Z.ai (Zhipu)GLM-5
37/100- ECI145.8
- Price$1.00 / $3.20
- Context205K
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 Qwen3.6 27B (56) and GLM-5 (37). It leads on price. Qwen3.6 27B wins 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 priceNemotron 3.5 Lightning 30B A3BNemotron 3.5 Lightning 30B A3B $0.087 · Qwen3.6 27B $1.35 · GLM-5 $1.55 per 1M tokens (3:1 blend)
- Longest contextQwen3.6 27B and Nemotron 3.5 Lightning 30B A3BQwen3.6 27B 262,144 · Nemotron 3.5 Lightning 30B A3B 262,144 · GLM-5 204,800 tokens
- Widest inputsQwen3.6 27BQwen3.6 27B: Text, Images, Audio, Video · Nemotron 3.5 Lightning 30B A3B: Text · GLM-5: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen3.6 27B | Nemotron 3.5 Lightning 30B A3B | GLM-5 |
|---|---|---|---|---|
| Price | 50% | 44 | 100 | 41 |
| Inputs & features | 30% | 90 | 45 | 35 |
| Context window | 20% | 37 | 37 | 32 |
| Overall | 100% | 56/100 | 71/100 | 37/100 |
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 | Qwen3.6 27BAlibaba (Qwen) | Nemotron 3.5 Lightning 30B A3BNVIDIA | GLM-5Z.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 146.5 (best) | — | 145.8 |
| ECI rank | #68 of 148 (best) | — | #74 of 148 |
| GPQA DiamondGraduate-level science questions | 85.9% | — | 87.8% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 35.1% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 91.1% (best) | — | 80.0% |
| SWE-bench VerifiedFixing real GitHub issues | — | — | 72.1% |
| Price per million tokens | |||
| Input | $0.60 | $0.05 (best) | $1.00 |
| Output | $3.60 | $0.20 (best) | $3.20 |
| Cached input | — | — | $0.20 |
| Blended (3:1) | $1.35 | $0.087 (best) | $1.55 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Median of 9 providers | Official Z.AI API |
| Limits | |||
| Context window | 262,144 tokens (best) | 262,144 tokens (best) | 204,800 tokens |
| Max output | 65,536 tokens | 262,144 tokens (best) | 131,072 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | Yes | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | qwen3.6-27b | nvidia/nemotron-3.5-lightning-30b-a3b | glm-5 |
| API providers | 27 (best) | 12 | 27 (best) |
| Released | Apr 22, 2026 | Aug 11, 2026 | Feb 12, 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.
- Qwen3.6 27B$13.20
- Nemotron 3.5 Lightning 30B A3B$0.90
- GLM-5$16.40
Which should you choose?
Which is better: Qwen3.6 27B, Nemotron 3.5 Lightning 30B A3B or GLM-5?
Nemotron 3.5 Lightning 30B A3B is the better all-round choice, scoring 71/100 against Qwen3.6 27B (56) and GLM-5 (37). It leads on price. Qwen3.6 27B wins 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, Qwen3.6 27B, Nemotron 3.5 Lightning 30B A3B or GLM-5?
Nemotron 3.5 Lightning 30B A3B is cheaper at $0.05 input / $0.20 output per million tokens (median across 9 API providers; free on Nvidia). Qwen3.6 27B costs $0.60 input / $3.60 output per million tokens (official Alibaba API price); 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 $0.087 per million tokens for Nemotron 3.5 Lightning 30B A3B versus $1.35 for Qwen3.6 27B (15× as much) and $1.55 for GLM-5 (18× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen3.6 27B has an ECI of 146.5, Nemotron 3.5 Lightning 30B A3B has not been scored yet and GLM-5 has an ECI of 145.8.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3.6 27B 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?
Qwen3.6 27B 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: Qwen3.6 27B up to 65,536, Nemotron 3.5 Lightning 30B A3B up to 262,144, GLM-5 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Qwen3.6 27B accepts text, images, audio and video; Nemotron 3.5 Lightning 30B A3B accepts text; GLM-5 accepts text. Qwen3.6 27B handles the widest range of inputs.
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. Qwen3.6 27B came out Apr 22, 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.
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