GLM-4.6V-Flash vs GLM-4.7 vs Qwen3 235B-A22B Thinking 2507
GLM-4.6V-Flash comes out ahead, 63 to 42 and 41 on our weighted score, and it is the cheaper option too.
- Our pick
Z.ai (Zhipu)GLM-4.6V-Flash
63/100- ECI—
- Price$0.161 / $0.559
- Context128K
- Z.ai (Zhipu)
GLM-4.7
41/100- ECI143.7
- Price$0.60 / $2.20
- Context205K
Alibaba (Qwen)Qwen3 235B-A22B Thinking 2507
42/100- ECI143.8
- Price$0.30 / $2.90
- Context262K
GLM-4.6V-Flash is our pick
GLM-4.6V-Flash is the better all-round choice, scoring 63/100 against Qwen3 235B-A22B Thinking 2507 (42) and GLM-4.7 (41). It leads on price and inputs & features. Qwen3 235B-A22B Thinking 2507 wins on context window. 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 priceGLM-4.6V-FlashGLM-4.6V-Flash $0.261 · 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 · GLM-4.6V-Flash 128,000 tokens
- Widest inputsGLM-4.6V-FlashGLM-4.6V-Flash: Text, Images, Video · 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 | GLM-4.6V-Flash | GLM-4.7 | Qwen3 235B-A22B Thinking 2507 |
|---|---|---|---|---|
| Price | 50% | 74 | 48 | 49 |
| Inputs & features | 30% | 70 | 35 | 35 |
| Context window | 20% | 24 | 32 | 37 |
| Overall | 100% | 63/100 | 41/100 | 42/100 |
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-4.6V-FlashZ.ai (Zhipu) | GLM-4.7Z.ai (Zhipu) | Qwen3 235B-A22B Thinking 2507Alibaba (Qwen) |
|---|---|---|---|
| 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% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 83.3% | 86.7% (best) |
| SimpleQA VerifiedShort factual questions | — | 32.2% | 40.4% (best) |
| Price per million tokens | |||
| Input | $0.161 (best) | $0.60 | $0.30 |
| Output | $0.559 (best) | $2.20 | $2.90 |
| Cached input | — | $0.11 | — |
| Blended (3:1) | $0.261 (best) | $1.00 | $0.95 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Official Z.AI API | Median of 11 providers |
| Limits | |||
| Context window | 128,000 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 | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | OpenApache 2.0 |
| API model ID | glm-4.6v-flash | glm-4.7 | — |
| API providers | 6 | 31 (best) | 12 |
| Released | Dec 8, 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.
- GLM-4.6V-Flash$2.73
- GLM-4.7$10.40
- Qwen3 235B-A22B Thinking 2507$8.80
Which should you choose?
Which is better: GLM-4.6V-Flash, GLM-4.7 or Qwen3 235B-A22B Thinking 2507?
GLM-4.6V-Flash is the better all-round choice, scoring 63/100 against Qwen3 235B-A22B Thinking 2507 (42) and GLM-4.7 (41). It leads on price and inputs & features. Qwen3 235B-A22B Thinking 2507 wins on context window. 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-4.6V-Flash, GLM-4.7 or Qwen3 235B-A22B Thinking 2507?
GLM-4.6V-Flash is cheaper at $0.161 input / $0.559 output per million tokens (median across 2 API providers; free on Z.AI). 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 $0.261 per million tokens for GLM-4.6V-Flash versus $0.95 for Qwen3 235B-A22B Thinking 2507 (3.6× as much) and $1.00 for GLM-4.7 (3.8× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.6V-Flash has not been scored yet, GLM-4.7 has an ECI of 143.7 and Qwen3 235B-A22B Thinking 2507 has an ECI of 143.8.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.6V-Flash, GLM-4.7 and Qwen3 235B-A22B Thinking 2507 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?
Qwen3 235B-A22B Thinking 2507 has the largest context window at 262,144 tokens, against 204,800 for GLM-4.7 and 128,000 for GLM-4.6V-Flash. Maximum output per response: GLM-4.6V-Flash 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?
GLM-4.6V-Flash accepts text, images and video; GLM-4.7 accepts text; Qwen3 235B-A22B Thinking 2507 accepts text. GLM-4.6V-Flash handles the widest range of inputs.
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. GLM-4.6V-Flash came out Dec 8, 2025; Qwen3 235B-A22B Thinking 2507 came out Jul 25, 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.
