DeepSeek V4 Pro vs GLM-5V-Turbo vs QwQ 32B
GLM-5V-Turbo comes out ahead, 49 to 45 and 43 on our weighted score, though QwQ 32B is 2.6× cheaper per token.
DeepSeekDeepSeek V4 Pro
45/100- ECI—
- Price$1.32 / $3.00
- Context1M
- Our pick
Z.ai (Zhipu)GLM-5V-Turbo
49/100- ECI—
- Price$1.20 / $4.00
- Context200K
Alibaba (Qwen)QwQ 32B
43/100- ECI137.6
- Price$0.66 / $1.00
- Context131K
GLM-5V-Turbo is our pick
GLM-5V-Turbo is the better all-round choice, scoring 49/100 against DeepSeek V4 Pro (45) and QwQ 32B (43). It leads on inputs & features. DeepSeek V4 Pro wins on context window. QwQ 32B 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 priceQwQ 32BQwQ 32B $0.745 · DeepSeek V4 Pro $1.74 · GLM-5V-Turbo $1.90 per 1M tokens (3:1 blend)
- Longest contextDeepSeek V4 ProDeepSeek V4 Pro 1,000,000 · GLM-5V-Turbo 200,000 · QwQ 32B 131,072 tokens
- Widest inputsGLM-5V-TurboDeepSeek V4 Pro: Text · GLM-5V-Turbo: Text, Images, PDFs, Video · QwQ 32B: Text
- Self-hostingDeepSeek V4 Pro and QwQ 32BPublishes downloadable weights
| Measure | Weight | DeepSeek V4 Pro | GLM-5V-Turbo | QwQ 32B |
|---|---|---|---|---|
| Price | 50% | 38 | 37 | 56 |
| Inputs & features | 30% | 45 | 80 | 35 |
| Context window | 20% | 60 | 32 | 24 |
| Overall | 100% | 45/100 | 49/100 | 43/100 |
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 | DeepSeek V4 ProDeepSeek | GLM-5V-TurboZ.ai (Zhipu) | QwQ 32BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | 137.6 |
| ECI rank | — | — | #109 of 148 |
| GPQA DiamondGraduate-level science questions | — | — | 65.3% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 59.2% |
| Price per million tokens | |||
| Input | $1.32 | $1.20 | $0.66 (best) |
| Output | $3.00 | $4.00 | $1.00 (best) |
| Cached input | — | $0.24 | — |
| Blended (3:1) | $1.74 | $1.90 | $0.745 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 49 providers | Official Z.AI API | Median of 1 providers |
| Limits | |||
| Context window | 1,000,000 tokens (best) | 200,000 tokens | 131,072 tokens |
| Max output | 384,000 tokens (best) | 131,072 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | — | glm-5v-turbo | — |
| API providers | 52 (best) | 14 | 1 |
| Released | Apr 24, 2026 | Apr 1, 2026 | Mar 5, 2025 |
| Knowledge cutoff | May 2025 | — | Apr 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- DeepSeek V4 Pro$19.20
- GLM-5V-Turbo$20.00
- QwQ 32B$8.60
Which should you choose?
Which is better: DeepSeek V4 Pro, GLM-5V-Turbo or QwQ 32B?
GLM-5V-Turbo is the better all-round choice, scoring 49/100 against DeepSeek V4 Pro (45) and QwQ 32B (43). It leads on inputs & features. DeepSeek V4 Pro wins on context window. QwQ 32B 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, DeepSeek V4 Pro, GLM-5V-Turbo or QwQ 32B?
QwQ 32B is cheaper at $0.66 input / $1.00 output per million tokens (median across 1 API provider). DeepSeek V4 Pro costs $1.32 input / $3.00 output per million tokens (median across 49 API providers); GLM-5V-Turbo costs $1.20 input / $4.00 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.745 per million tokens for QwQ 32B versus $1.74 for DeepSeek V4 Pro (2.3× as much) and $1.90 for GLM-5V-Turbo (2.6× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. DeepSeek V4 Pro has not been scored yet, GLM-5V-Turbo has not been scored yet and QwQ 32B has an ECI of 137.6.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek V4 Pro, GLM-5V-Turbo and QwQ 32B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
DeepSeek V4 Pro has the largest context window at 1,000,000 tokens, against 200,000 for GLM-5V-Turbo and 131,072 for QwQ 32B. Maximum output per response: DeepSeek V4 Pro up to 384,000, GLM-5V-Turbo up to 131,072, QwQ 32B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
DeepSeek V4 Pro accepts text; GLM-5V-Turbo accepts text, images, PDFs and video; QwQ 32B accepts text. GLM-5V-Turbo handles the widest range of inputs.
Are any of these open source?
DeepSeek V4 Pro and QwQ 32B publishes its weights and can be self-hosted; GLM-5V-Turbo is proprietary.
Which is newer?
DeepSeek V4 Pro is the newest, released Apr 24, 2026. GLM-5V-Turbo came out Apr 1, 2026; QwQ 32B came out Mar 5, 2025. Knowledge cutoff: DeepSeek V4 Pro May 2025, QwQ 32B Apr 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.
