DeepSeek R1 0528 vs GLM-4.7 vs Qwen3 235B-A22B Thinking 2507
Too close to call on our weighted score (Qwen3 235B-A22B Thinking 2507 55, GLM-4.7 55, DeepSeek R1 0528 53). The right pick depends on what you value most.
DeepSeek
DeepSeek R1 0528
53/100- ECI141.2
- Price$0.512 / $2.19
- Context164K
Z.ai (Zhipu)
GLM-4.7
55/100- ECI143.7
- Price$0.60 / $2.20
- Context205K
Alibaba (Qwen)
Qwen3 235B-A22B Thinking 2507
55/100- ECI143.8
- Price$0.30 / $2.90
- Context262K
Too close to call
It is close. Our weighted score puts them within a point (Qwen3 235B-A22B Thinking 2507 55/100, GLM-4.7 55/100, DeepSeek R1 0528 53/100), so choose by what matters most for your work: Qwen3 235B-A22B Thinking 2507 for raw capability and DeepSeek R1 0528 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3 235B-A22B Thinking 2507Capabilities Index (ECI): Qwen3 235B-A22B Thinking 2507 143.8 · GLM-4.7 143.7 · DeepSeek R1 0528 141.2
- Lowest priceDeepSeek R1 0528DeepSeek R1 0528 $0.932 · Qwen3 235B-A22B Thinking 2507 $0.95 · GLM-4.7 $1.00 per 1M tokens (3:1 blend)
- Longest contextQwen3 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 262,144 · GLM-4.7 204,800 · DeepSeek R1 0528 163,840 tokens
- Widest inputsSame inputsDeepSeek R1 0528: Text · GLM-4.7: Text · Qwen3 235B-A22B Thinking 2507: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | DeepSeek R1 0528 | GLM-4.7 | Qwen3 235B-A22B Thinking 2507 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 66 | 69 | 69 |
| Price | 25% | 49 | 48 | 49 |
| Inputs & features | 15% | 35 | 35 | 35 |
| Context window | 10% | 28 | 32 | 37 |
| Overall | 100% | 53/100 | 55/100 | 55/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | |||
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 141.2 | 143.7 | 143.8 (best) |
| ECI rank | #108 of 165 | #94 of 165 | #93 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 76.3% | 83.3% (best) | 80.1% |
| OTIS Mock AIME 2024–2025Competition mathematics | 66.4% | 83.3% | 86.7% (best) |
| SimpleQA VerifiedShort factual questions | — | 32.2% | 40.4% (best) |
| Price per million tokens | |||
| Input | $0.512 | $0.60 | $0.30 (best) |
| Output | $2.19 (best) | $2.20 | $2.90 |
| Cached input | — | $0.11 | — |
| Blended (3:1) | $0.932 (best) | $1.00 | $0.95 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 16 providers | Official Z.AI API | Median of 11 providers |
| Limits | |||
| Context window | 163,840 tokens | 204,800 tokens | 262,144 tokens (best) |
| Max output | 32,768 tokens | 131,072 tokens (best) | 81,920 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 | No | No |
| Availability | |||
| Weights | OpenMIT | Open | OpenApache 2.0 |
| API model ID | — | glm-4.7 | — |
| API providers | 16 | 31 (best) | 12 |
| Released | May 28, 2025 | Dec 22, 2025 | Jul 25, 2025 |
| 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.
DeepSeek R1 0528$9.50
GLM-4.7$10.40
Qwen3 235B-A22B Thinking 2507$8.80
Which should you choose?
Which is better: DeepSeek R1 0528, GLM-4.7 or Qwen3 235B-A22B Thinking 2507?
It is close. Our weighted score puts them within a point (Qwen3 235B-A22B Thinking 2507 55/100, GLM-4.7 55/100, DeepSeek R1 0528 53/100), so choose by what matters most for your work: Qwen3 235B-A22B Thinking 2507 for raw capability and DeepSeek R1 0528 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, DeepSeek R1 0528, GLM-4.7 or Qwen3 235B-A22B Thinking 2507?
DeepSeek R1 0528 is cheaper at $0.512 input / $2.19 output per million tokens (median across 16 API providers). Qwen3 235B-A22B Thinking 2507 costs $0.30 input / $2.90 output per million tokens (median across 11 API providers); GLM-4.7 costs $0.60 input / $2.20 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.932 per million tokens for DeepSeek R1 0528 versus $0.95 for Qwen3 235B-A22B Thinking 2507 (1× as much) and $1.00 for GLM-4.7 (1.1× as much).
Which scores higher on benchmarks?
Qwen3 235B-A22B Thinking 2507 scores higher on the Capabilities Index (ECI): Qwen3 235B-A22B Thinking 2507 143.8 (#93 of 165), GLM-4.7 143.7 (#94 of 165) and DeepSeek R1 0528 141.2 (#108 of 165). The confidence ranges of the top two overlap (141.9–145.2 vs 141.4–145.8), so treat the gap as small. On individual benchmarks: GPQA Diamond — GLM-4.7 83.3%, Qwen3 235B-A22B Thinking 2507 80.1%, DeepSeek R1 0528 76.3%; OTIS Mock AIME 2024–2025 — Qwen3 235B-A22B Thinking 2507 86.7%, GLM-4.7 83.3%, DeepSeek R1 0528 66.4%.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek R1 0528, GLM-4.7 and Qwen3 235B-A22B Thinking 2507 yet, so there is no like-for-like coding score. On overall capability, Qwen3 235B-A22B Thinking 2507 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 235B-A22B Thinking 2507 has the largest context window at 262,144 tokens, against 204,800 for GLM-4.7 and 163,840 for DeepSeek R1 0528. Maximum output per response: DeepSeek R1 0528 up to 32,768, GLM-4.7 up to 131,072, Qwen3 235B-A22B Thinking 2507 up to 81,920 tokens.
Which can read images, PDFs, audio or video?
DeepSeek R1 0528 accepts text; GLM-4.7 accepts text; Qwen3 235B-A22B Thinking 2507 accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (MIT and Apache 2.0), so you can self-host them.
Which is newer?
GLM-4.7 is the newest, released Dec 22, 2025. Qwen3 235B-A22B Thinking 2507 came out Jul 25, 2025; DeepSeek R1 0528 came out May 28, 2025. Knowledge cutoff: GLM-4.7 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 Nov 2, 2026.