Qwen3 235B-A22B vs GLM-5.1 vs DeepSeek-R1
GLM-5.1 comes out ahead, 57 to 51 and 51 on our weighted score, though DeepSeek-R1 is 45% cheaper per token.
Alibaba (Qwen)Qwen3 235B-A22B
51/100- ECI139.4
- Price$0.70 / $2.80
- Context131K
- Our pick
Z.ai (Zhipu)GLM-5.1
57/100- ECI149.9
- Price$1.40 / $4.40
- Context200K
DeepSeekDeepSeek-R1
51/100- ECI139.0
- Price$0.70 / $2.60
- Context128K
GLM-5.1 is our pick
GLM-5.1 is the better all-round choice, scoring 57/100 against Qwen3 235B-A22B (51) and DeepSeek-R1 (51). It leads on capability, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGLM-5.1Capabilities Index (ECI): GLM-5.1 149.9 · Qwen3 235B-A22B 139.4 · DeepSeek-R1 139.0
- Lowest priceDeepSeek-R1DeepSeek-R1 $1.18 · Qwen3 235B-A22B $1.23 · GLM-5.1 $2.15 per 1M tokens (3:1 blend)
- Longest contextGLM-5.1GLM-5.1 200,000 · Qwen3 235B-A22B 131,072 · DeepSeek-R1 128,000 tokens
- Widest inputsSame inputsQwen3 235B-A22B: Text · GLM-5.1: Text · DeepSeek-R1: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen3 235B-A22B | GLM-5.1 | DeepSeek-R1 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 65 | 78 | 64 |
| Price | 25% | 46 | 34 | 47 |
| Inputs & features | 15% | 35 | 45 | 35 |
| Context window | 10% | 24 | 32 | 24 |
| Overall | 100% | 51/100 | 57/100 | 51/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen3 235B-A22BAlibaba (Qwen) | GLM-5.1Z.ai (Zhipu) | DeepSeek-R1DeepSeek |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 139.4 | 149.9 (best) | 139.0 |
| ECI rank | #103 of 148 | #51 of 148 (best) | #104 of 148 |
| GPQA DiamondGraduate-level science questions | 70.7% | 89.9% (best) | 71.7% |
| FrontierMath Tiers 1–3Research-level mathematics | — | 36.8% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 93.3% (best) | 53.3% |
| SWE-bench VerifiedFixing real GitHub issues | — | 74.2% | — |
| SimpleQA VerifiedShort factual questions | — | 34.0% | — |
| Price per million tokens | |||
| Input | $0.70 (best) | $1.40 | $0.70 (best) |
| Output | $2.80 | $4.40 | $2.60 (best) |
| Cached input | — | $0.26 | — |
| Blended (3:1) | $1.23 | $2.15 | $1.18 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Official Z.AI API | Median of 11 providers |
| Limits | |||
| Context window | 131,072 tokens | 200,000 tokens (best) | 128,000 tokens |
| Max output | 16,384 tokens | 131,072 tokens (best) | 32,768 tokens |
| 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 | Yes | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | qwen3-235b-a22b | glm-5.1 | — |
| API providers | 7 | 40 (best) | 12 |
| Released | Apr 28, 2025 | Apr 7, 2026 | Jan 20, 2025 |
| 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 235B-A22B$12.60
- GLM-5.1$22.80
- DeepSeek-R1$12.20
Which should you choose?
Which is better: Qwen3 235B-A22B, GLM-5.1 or DeepSeek-R1?
GLM-5.1 is the better all-round choice, scoring 57/100 against Qwen3 235B-A22B (51) and DeepSeek-R1 (51). It leads on capability, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen3 235B-A22B, GLM-5.1 or DeepSeek-R1?
DeepSeek-R1 is cheaper at $0.70 input / $2.60 output per million tokens (median across 11 API providers). Qwen3 235B-A22B costs $0.70 input / $2.80 output per million tokens (official Alibaba API price); GLM-5.1 costs $1.40 input / $4.40 output per million tokens (official Z.AI 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 $1.23 for Qwen3 235B-A22B (1× as much) and $2.15 for GLM-5.1 (1.8× 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 235B-A22B 139.4 (#103 of 148) and DeepSeek-R1 139.0 (#104 of 148). Their confidence ranges do not overlap (148.0–151.6 vs 135.2–140.8), so the gap is a real one. On individual benchmarks: GPQA Diamond — GLM-5.1 89.9%, DeepSeek-R1 71.7%, Qwen3 235B-A22B 70.7%.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3 235B-A22B and 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?
GLM-5.1 has the largest context window at 200,000 tokens, against 131,072 for Qwen3 235B-A22B and 128,000 for DeepSeek-R1. Maximum output per response: Qwen3 235B-A22B up to 16,384, GLM-5.1 up to 131,072, DeepSeek-R1 up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Qwen3 235B-A22B accepts text; GLM-5.1 accepts text; DeepSeek-R1 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?
GLM-5.1 is the newest, released Apr 7, 2026. Qwen3 235B-A22B came out Apr 28, 2025; DeepSeek-R1 came out Jan 20, 2025. Knowledge cutoff: Qwen3 235B-A22B 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.
