GLM-5 vs Laguna XS 2.1 vs Qwen3.6 27B
Laguna XS 2.1 comes out ahead, 68 to 56 and 37 on our weighted score, and it is the cheaper option too.
Z.ai (Zhipu)GLM-5
37/100- ECI145.8
- Price$1.00 / $3.20
- Context205K
- Our pick
PoolsideLaguna XS 2.1
68/100- ECI—
- Price$0.06 / $0.12
- Context262K
Alibaba (Qwen)Qwen3.6 27B
56/100- ECI146.5
- Price$0.60 / $3.60
- Context262K
Laguna XS 2.1 is our pick
Laguna XS 2.1 is the better all-round choice, scoring 68/100 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 priceLaguna XS 2.1Laguna XS 2.1 $0.075 · Qwen3.6 27B $1.35 · GLM-5 $1.55 per 1M tokens (3:1 blend)
- Longest contextLaguna XS 2.1 and Qwen3.6 27BLaguna XS 2.1 262,144 · Qwen3.6 27B 262,144 · GLM-5 204,800 tokens
- Widest inputsQwen3.6 27BGLM-5: Text · Laguna XS 2.1: Text · Qwen3.6 27B: Text, Images, Audio, Video
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-5 | Laguna XS 2.1 | Qwen3.6 27B |
|---|---|---|---|---|
| Price | 50% | 41 | 100 | 44 |
| Inputs & features | 30% | 35 | 35 | 90 |
| Context window | 20% | 32 | 37 | 37 |
| Overall | 100% | 37/100 | 68/100 | 56/100 |
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 | Qwen3.6 27BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.8 | — | 146.5 (best) |
| ECI rank | #74 of 148 | — | #68 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 87.8% (best) | — | 85.9% |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 35.1% |
| OTIS Mock AIME 2024–2025Competition mathematics | 80.0% | — | 91.1% (best) |
| SWE-bench VerifiedFixing real GitHub issues | 72.1% | — | — |
| Price per million tokens | |||
| Input | $1.00 | $0.06 (best) | $0.60 |
| Output | $3.20 | $0.12 (best) | $3.60 |
| Cached input | $0.20 | — | — |
| Blended (3:1) | $1.55 | $0.075 (best) | $1.35 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 1 providers | Official Alibaba API |
| Limits | |||
| Context window | 204,800 tokens | 262,144 tokens (best) | 262,144 tokens (best) |
| Max output | 131,072 tokens (best) | 32,768 tokens | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | Yes |
| Video | No | No | Yes |
| 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 | qwen3.6-27b |
| API providers | 27 (best) | 4 | 27 (best) |
| Released | Feb 12, 2026 | Jul 2, 2026 | Apr 22, 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
- Qwen3.6 27B$13.20
Which should you choose?
Which is better: GLM-5, Laguna XS 2.1 or Qwen3.6 27B?
Laguna XS 2.1 is the better all-round choice, scoring 68/100 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, GLM-5, Laguna XS 2.1 or Qwen3.6 27B?
Laguna XS 2.1 is cheaper at $0.06 input / $0.12 output per million tokens (median across 1 API provider; free on Poolside). 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 $0.075 per million tokens for Laguna XS 2.1 versus $1.35 for Qwen3.6 27B (18× 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 Qwen3.6 27B has an ECI of 146.5.
Which is better for coding?
There are no published SWE-bench Verified results for Laguna XS 2.1 and Qwen3.6 27B 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 Qwen3.6 27B 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, Qwen3.6 27B up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GLM-5 accepts text; Laguna XS 2.1 accepts text; Qwen3.6 27B accepts text, images, audio and video. 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?
Laguna XS 2.1 is the newest, released Jul 2, 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.
