Qwen3.6 Max Preview vs DeepSeek-R1 vs GLM-5.1
GLM-5.1 comes out ahead, 57 to 54 and 51 on our weighted score, though DeepSeek-R1 is 45% cheaper per token.
Alibaba (Qwen)
Qwen3.6 Max Preview
54/100- ECI149.2
- Price$1.30 / $7.80
- Context262K
DeepSeek
DeepSeek-R1
51/100- ECI139.0
- Price$0.70 / $2.60
- Context128K
- Our pick
Z.ai (Zhipu)
GLM-5.1
57/100- ECI149.9
- Price$1.40 / $4.40
- Context200K
GLM-5.1 is our pick
GLM-5.1 is the better all-round choice, scoring 57/100 against Qwen3.6 Max Preview (54) and DeepSeek-R1 (51). It leads on inputs & features. Qwen3.6 Max Preview wins on context window. DeepSeek-R1 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGLM-5.1Capabilities Index (ECI): GLM-5.1 149.9 · Qwen3.6 Max Preview 149.2 · DeepSeek-R1 139.0
- Lowest priceDeepSeek-R1DeepSeek-R1 $1.18 · GLM-5.1 $2.15 · Qwen3.6 Max Preview $2.92 per 1M tokens (3:1 blend)
- Longest contextQwen3.6 Max PreviewQwen3.6 Max Preview 262,144 · GLM-5.1 200,000 · DeepSeek-R1 128,000 tokens
- Widest inputsSame inputsQwen3.6 Max Preview: Text · DeepSeek-R1: Text · GLM-5.1: Text
- Self-hostingDeepSeek-R1 and GLM-5.1Publishes downloadable weights
| Measure | Weight | Qwen3.6 Max Preview | DeepSeek-R1 | GLM-5.1 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 77 | 64 | 78 |
| Price | 25% | 28 | 47 | 34 |
| Inputs & features | 15% | 35 | 35 | 45 |
| Context window | 10% | 37 | 24 | 32 |
| Overall | 100% | 54/100 | 51/100 | 57/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | |||
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 149.2 | 139.0 | 149.9 (best) |
| ECI rank | #54 of 148 | #104 of 148 | #51 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 87.4% | 71.7% | 89.9% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 36.8% |
| OTIS Mock AIME 2024–2025Competition mathematics | 91.1% | 53.3% | 93.3% (best) |
| SWE-bench VerifiedFixing real GitHub issues | 76.7% (best) | — | 74.2% |
| SimpleQA VerifiedShort factual questions | 52.0% (best) | — | 34.0% |
| Price per million tokens | |||
| Input | $1.30 | $0.70 (best) | $1.40 |
| Output | $7.80 | $2.60 (best) | $4.40 |
| Cached input | $0.13 (best) | — | $0.26 |
| Blended (3:1) | $2.92 | $1.18 (best) | $2.15 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Median of 11 providers | Official Z.AI API |
| Limits | |||
| Context window | 262,144 tokens (best) | 128,000 tokens | 200,000 tokens |
| Max output | 65,536 tokens | 32,768 tokens | 131,072 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 | Proprietary | Open | Open |
| API model ID | qwen3.6-max-preview | — | glm-5.1 |
| API providers | 10 | 12 | 40 (best) |
| Released | Apr 20, 2026 | Jan 20, 2025 | Apr 7, 2026 |
| Knowledge cutoff | Apr 2025 | Jul 2024 | — |
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 Max Preview$28.60
DeepSeek-R1$12.20
GLM-5.1$22.80
Which should you choose?
Which is better: Qwen3.6 Max Preview, DeepSeek-R1 or GLM-5.1?
GLM-5.1 is the better all-round choice, scoring 57/100 against Qwen3.6 Max Preview (54) and DeepSeek-R1 (51). It leads on inputs & features. Qwen3.6 Max Preview wins on context window. DeepSeek-R1 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen3.6 Max Preview, DeepSeek-R1 or GLM-5.1?
DeepSeek-R1 is cheaper at $0.70 input / $2.60 output per million tokens (median across 11 API providers). GLM-5.1 costs $1.40 input / $4.40 output per million tokens (official Z.AI API price); Qwen3.6 Max Preview costs $1.30 input / $7.80 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $1.18 per million tokens for DeepSeek-R1 versus $2.15 for GLM-5.1 (1.8× as much) and $2.92 for Qwen3.6 Max Preview (2.5× as much).
Which scores higher on benchmarks?
GLM-5.1 scores higher on the Capabilities Index (ECI): GLM-5.1 149.9 (#51 of 148), Qwen3.6 Max Preview 149.2 (#54 of 148) and DeepSeek-R1 139.0 (#104 of 148). The confidence ranges of the top two overlap (148.0–151.6 vs 147.6–152.0), so treat the gap as small. On individual benchmarks: GPQA Diamond — GLM-5.1 89.9%, Qwen3.6 Max Preview 87.4%, DeepSeek-R1 71.7%; OTIS Mock AIME 2024–2025 — GLM-5.1 93.3%, Qwen3.6 Max Preview 91.1%, DeepSeek-R1 53.3%.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek-R1 yet, so there is no like-for-like coding score. On overall capability, GLM-5.1 leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen3.6 Max Preview has the largest context window at 262,144 tokens, against 200,000 for GLM-5.1 and 128,000 for DeepSeek-R1. Maximum output per response: Qwen3.6 Max Preview up to 65,536, DeepSeek-R1 up to 32,768, GLM-5.1 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Qwen3.6 Max Preview accepts text; DeepSeek-R1 accepts text; GLM-5.1 accepts text. They handle the same number of input types.
Are any of these open source?
DeepSeek-R1 and GLM-5.1 publishes its weights and can be self-hosted; Qwen3.6 Max Preview is proprietary.
Which is newer?
Qwen3.6 Max Preview is the newest, released Apr 20, 2026. GLM-5.1 came out Apr 7, 2026; DeepSeek-R1 came out Jan 20, 2025. Knowledge cutoff: Qwen3.6 Max Preview Apr 2025, DeepSeek-R1 Jul 2024.
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.