GLM-5 vs DeepSeek OCR 2 vs Qwen3.5 397B-A17B
Qwen3.5 397B-A17B comes out ahead, 56 to 37 and 32 on our weighted score, though DeepSeek OCR 2 is 23% cheaper per token.
Z.ai (Zhipu)GLM-5
37/100- ECI145.8
- Price$1.00 / $3.20
- Context205K
DeepSeekDeepSeek OCR 2
32/100- ECI—
- Price$0.89 / $1.47
- Context8K
- Our pick
Alibaba (Qwen)Qwen3.5 397B-A17B
56/100- ECI146.7
- Price$0.60 / $3.60
- Context262K
Qwen3.5 397B-A17B is our pick
Qwen3.5 397B-A17B is the better all-round choice, scoring 56/100 against GLM-5 (37) and DeepSeek OCR 2 (32). It leads on inputs & features and context window. DeepSeek OCR 2 wins on price. 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 priceDeepSeek OCR 2DeepSeek OCR 2 $1.03 · Qwen3.5 397B-A17B $1.35 · GLM-5 $1.55 per 1M tokens (3:1 blend)
- Longest contextQwen3.5 397B-A17BQwen3.5 397B-A17B 262,144 · GLM-5 204,800 · DeepSeek OCR 2 8,192 tokens
- Widest inputsQwen3.5 397B-A17BGLM-5: Text · DeepSeek OCR 2: Text, Images · Qwen3.5 397B-A17B: Text, Images, Audio, Video
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-5 | DeepSeek OCR 2 | Qwen3.5 397B-A17B |
|---|---|---|---|---|
| Price | 50% | 41 | 49 | 44 |
| Inputs & features | 30% | 35 | 25 | 90 |
| Context window | 20% | 32 | 0 | 37 |
| Overall | 100% | 37/100 | 32/100 | 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) | DeepSeek OCR 2DeepSeek | Qwen3.5 397B-A17BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.8 | — | 146.7 (best) |
| ECI rank | #74 of 148 | — | #67 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 87.8% (best) | — | 86.4% |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 31.2% |
| OTIS Mock AIME 2024–2025Competition mathematics | 80.0% | — | 88.9% (best) |
| SWE-bench VerifiedFixing real GitHub issues | 72.1% | — | — |
| Price per million tokens | |||
| Input | $1.00 | $0.89 | $0.60 (best) |
| Output | $3.20 | $1.47 (best) | $3.60 |
| Cached input | $0.20 | — | — |
| Blended (3:1) | $1.55 | $1.03 (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 | 8,192 tokens | 262,144 tokens (best) |
| Max output | 131,072 tokens (best) | 8,192 tokens | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | Yes |
| Video | No | No | Yes |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | No | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-5 | — | qwen3.5-397b-a17b |
| API providers | 27 (best) | 2 | 23 |
| Released | Feb 12, 2026 | Jan 27, 2026 | Feb 15, 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
- DeepSeek OCR 2$11.83
- Qwen3.5 397B-A17B$13.20
Which should you choose?
Which is better: GLM-5, DeepSeek OCR 2 or Qwen3.5 397B-A17B?
Qwen3.5 397B-A17B is the better all-round choice, scoring 56/100 against GLM-5 (37) and DeepSeek OCR 2 (32). It leads on inputs & features and context window. DeepSeek OCR 2 wins on price. 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, DeepSeek OCR 2 or Qwen3.5 397B-A17B?
DeepSeek OCR 2 is cheaper at $0.89 input / $1.47 output per million tokens (median across 1 API provider). Qwen3.5 397B-A17B 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 $1.03 per million tokens for DeepSeek OCR 2 versus $1.35 for Qwen3.5 397B-A17B (1.3× as much) and $1.55 for GLM-5 (1.5× 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, DeepSeek OCR 2 has not been scored yet and Qwen3.5 397B-A17B has an ECI of 146.7.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek OCR 2 and Qwen3.5 397B-A17B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that DeepSeek OCR 2 does not support tool calling, which most coding agents need.
Which has the bigger context window?
Qwen3.5 397B-A17B has the largest context window at 262,144 tokens, against 204,800 for GLM-5 and 8,192 for DeepSeek OCR 2. Maximum output per response: GLM-5 up to 131,072, DeepSeek OCR 2 up to 8,192, Qwen3.5 397B-A17B up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GLM-5 accepts text; DeepSeek OCR 2 accepts text and images; Qwen3.5 397B-A17B accepts text, images, audio and video. Qwen3.5 397B-A17B 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?
Qwen3.5 397B-A17B is the newest, released Feb 15, 2026. GLM-5 came out Feb 12, 2026; DeepSeek OCR 2 came out Jan 27, 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.
