GLM-4.7 vs Qwen3 235B-A22B Thinking 2507 vs Seed-OSS-36B-Instruct
Too close to call on our weighted score (Qwen3 235B-A22B Thinking 2507 67, GLM-4.7 67, Seed-OSS-36B-Instruct 60). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-4.7
67/100- ECI143.7
- Price$0.60 / $2.20
- Context205K
Alibaba (Qwen)Qwen3 235B-A22B Thinking 2507
67/100- ECI143.8
- Price$0.30 / $2.90
- Context262K
ByteDance SeedSeed-OSS-36B-Instruct
60/100- ECI—
- Price—
- Context524K
Too close to call
It is close. Our weighted score puts them within a point (Qwen3 235B-A22B Thinking 2507 67/100, GLM-4.7 67/100, Seed-OSS-36B-Instruct 60/100), so choose by what matters most for your work: Qwen3 235B-A22B Thinking 2507 for raw capability and Seed-OSS-36B-Instruct for long inputs. The score weighs capability 67%, inputs & features 20%, context window 13%. Capability uses the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025), because Seed-OSS-36B-Instruct has no Capabilities Index score yet.
- CapabilityQwen3 235B-A22B Thinking 2507Shared benchmarks: Qwen3 235B-A22B Thinking 2507 83.4% · GLM-4.7 83.3% · Seed-OSS-36B-Instruct 69.5%
- Lowest priceQwen3 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 $0.95 · GLM-4.7 $1.00 per 1M tokens (3:1 blend) · Seed-OSS-36B-Instruct unpriced
- Longest contextSeed-OSS-36B-InstructSeed-OSS-36B-Instruct 524,288 · Qwen3 235B-A22B Thinking 2507 262,144 · GLM-4.7 204,800 tokens
- Widest inputsSame inputsGLM-4.7: Text · Qwen3 235B-A22B Thinking 2507: Text · Seed-OSS-36B-Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.7 | Qwen3 235B-A22B Thinking 2507 | Seed-OSS-36B-Instruct |
|---|---|---|---|---|
| CapabilityShared benchmarks | 67% | 83 | 83 | 70 |
| Inputs & features | 20% | 35 | 35 | 35 |
| Context window | 13% | 32 | 37 | 49 |
| Overall | 100% | 67/100 | 67/100 | 60/100 |
Left out because at least one model lacks the data: price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-4.7Z.ai (Zhipu) | Qwen3 235B-A22B Thinking 2507Alibaba (Qwen) | Seed-OSS-36B-InstructByteDance Seed |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 143.7 | 143.8 (best) | — |
| ECI rank | #94 of 165 | #93 of 165 (best) | — |
| GPQA DiamondGraduate-level science questions | 83.3% (best) | 80.1% | 71.5% |
| OTIS Mock AIME 2024–2025Competition mathematics | 83.3% | 86.7% (best) | 67.5% |
| SimpleQA VerifiedShort factual questions | 32.2% | 40.4% (best) | — |
| Price per million tokens | |||
| Input | $0.60 | $0.30 (best) | — |
| Output | $2.20 (best) | $2.90 | — |
| Cached input | $0.11 | — | — |
| Blended (3:1) | $1.00 | $0.95 (best) | — |
| Long-context rate | Same rate | Same rate | — |
| Price source | Official Z.AI API | Median of 11 providers | — |
| Limits | |||
| Context window | 204,800 tokens | 262,144 tokens | 524,288 tokens (best) |
| Max output | 131,072 tokens | 81,920 tokens | 524,288 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 | No |
| Availability | |||
| Weights | Open | OpenApache 2.0 | OpenApache 2.0 |
| API model ID | glm-4.7 | — | — |
| API providers | 31 (best) | 12 | — |
| Released | Dec 22, 2025 | Jul 25, 2025 | Aug 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.
- GLM-4.7$10.40
- Qwen3 235B-A22B Thinking 2507$8.80
- Seed-OSS-36B-Instruct—
Which should you choose?
Which is better: GLM-4.7, Qwen3 235B-A22B Thinking 2507 or Seed-OSS-36B-Instruct?
It is close. Our weighted score puts them within a point (Qwen3 235B-A22B Thinking 2507 67/100, GLM-4.7 67/100, Seed-OSS-36B-Instruct 60/100), so choose by what matters most for your work: Qwen3 235B-A22B Thinking 2507 for raw capability and Seed-OSS-36B-Instruct for long inputs. The score weighs capability 67%, inputs & features 20%, context window 13%. Capability uses the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025), because Seed-OSS-36B-Instruct has no Capabilities Index score yet.
Which is cheaper, GLM-4.7, Qwen3 235B-A22B Thinking 2507 or Seed-OSS-36B-Instruct?
Qwen3 235B-A22B Thinking 2507 is cheaper at $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 $0.95 per million tokens for Qwen3 235B-A22B Thinking 2507 versus $1.00 for GLM-4.7 (1.1× as much). Seed-OSS-36B-Instruct has no published per-token price.
Which scores higher on benchmarks?
Not every model here has a Capabilities Index score, so we compare the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025): Qwen3 235B-A22B Thinking 2507 83.4%, GLM-4.7 83.3% and Seed-OSS-36B-Instruct 69.5%. On individual benchmarks: GPQA Diamond — GLM-4.7 83.3%, Qwen3 235B-A22B Thinking 2507 80.1%, Seed-OSS-36B-Instruct 71.5%; OTIS Mock AIME 2024–2025 — Qwen3 235B-A22B Thinking 2507 86.7%, GLM-4.7 83.3%, Seed-OSS-36B-Instruct 67.5%.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.7, Qwen3 235B-A22B Thinking 2507 and Seed-OSS-36B-Instruct 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?
Seed-OSS-36B-Instruct has the largest context window at 524,288 tokens, against 262,144 for Qwen3 235B-A22B Thinking 2507 and 204,800 for GLM-4.7. Maximum output per response: GLM-4.7 up to 131,072, Qwen3 235B-A22B Thinking 2507 up to 81,920, Seed-OSS-36B-Instruct up to 524,288 tokens.
Which can read images, PDFs, audio or video?
GLM-4.7 accepts text; Qwen3 235B-A22B Thinking 2507 accepts text; Seed-OSS-36B-Instruct accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (Apache 2.0), so you can self-host them.
Which is newer?
GLM-4.7 is the newest, released Dec 22, 2025. Seed-OSS-36B-Instruct came out Aug 20, 2025; Qwen3 235B-A22B Thinking 2507 came out Jul 25, 2025. Knowledge cutoff: GLM-4.7 Apr 2025, Seed-OSS-36B-Instruct 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 Nov 2, 2026.
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