GLM-4.7-FlashX vs Qwen Flash vs Voxtral Small 24B 2507
Qwen Flash comes out ahead, 68 to 61 and 55 on our weighted score, and it is the cheaper option too.
Z.ai (Zhipu)
GLM-4.7-FlashX
61/100- ECI—
- Price$0.07 / $0.40
- Context200K
- Our pick
Alibaba (Qwen)
Qwen Flash
68/100- ECI—
- Price$0.05 / $0.40
- Context1M
Mistral AI
Voxtral Small 24B 2507
55/100- ECI—
- Price$0.10 / $0.30
- Context33K
Qwen Flash is our pick
Qwen Flash is the better all-round choice, scoring 68/100 against GLM-4.7-FlashX (61) and Voxtral Small 24B 2507 (55). It leads on price and 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 priceQwen FlashQwen Flash $0.138 · Voxtral Small 24B 2507 $0.15 · GLM-4.7-FlashX $0.152 per 1M tokens (3:1 blend)
- Longest contextQwen FlashQwen Flash 1,000,000 · GLM-4.7-FlashX 200,000 · Voxtral Small 24B 2507 32,768 tokens
- Widest inputsVoxtral Small 24B 2507GLM-4.7-FlashX: Text · Qwen Flash: Text · Voxtral Small 24B 2507: Text, Audio
- Self-hostingGLM-4.7-FlashX and Voxtral Small 24B 2507Publishes downloadable weights (Apache 2.0)
| Measure | Weight | GLM-4.7-FlashX | Qwen Flash | Voxtral Small 24B 2507 |
|---|---|---|---|---|
| Price | 50% | 89 | 91 | 89 |
| Inputs & features | 30% | 35 | 35 | 35 |
| Context window | 20% | 32 | 60 | 0 |
| Overall | 100% | 61/100 | 68/100 | 55/100 |
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 | |||
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.07 | $0.05 (best) | $0.10 |
| Output | $0.40 | $0.40 | $0.30 (best) |
| Cached input | $0.01 | — | — |
| Blended (3:1) | $0.152 | $0.138 (best) | $0.15 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Alibaba API | Official Mistral API |
| Limits | |||
| Context window | 200,000 tokens | 1,000,000 tokens (best) | 32,768 tokens |
| Max output | 131,072 tokens (best) | 32,768 tokens | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | Yes |
| Video | No | No | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | OpenApache 2.0 |
| API model ID | glm-4.7-flashx | qwen-flash | voxtral-small-latest |
| API providers | 8 (best) | 6 | 7 |
| Released | Jan 19, 2026 | Jul 28, 2025 | Jul 15, 2025 |
| Knowledge cutoff | Apr 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.
GLM-4.7-FlashX$1.50
Qwen Flash$1.30
Voxtral Small 24B 2507$1.60
Which should you choose?
Which is better: GLM-4.7-FlashX, Qwen Flash or Voxtral Small 24B 2507?
Qwen Flash is the better all-round choice, scoring 68/100 against GLM-4.7-FlashX (61) and Voxtral Small 24B 2507 (55). It leads on price and 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-4.7-FlashX, Qwen Flash or Voxtral Small 24B 2507?
Qwen Flash is cheaper at $0.05 input / $0.40 output per million tokens (official Alibaba API price). Voxtral Small 24B 2507 costs $0.10 input / $0.30 output per million tokens (official Mistral API price); GLM-4.7-FlashX costs $0.07 input / $0.40 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.138 per million tokens for Qwen Flash versus $0.15 for Voxtral Small 24B 2507 (1.1× as much) and $0.152 for GLM-4.7-FlashX (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.7-FlashX has not been scored yet, Qwen Flash has not been scored yet and Voxtral Small 24B 2507 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.7-FlashX, Qwen Flash and Voxtral Small 24B 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?
Qwen Flash has the largest context window at 1,000,000 tokens, against 200,000 for GLM-4.7-FlashX and 32,768 for Voxtral Small 24B 2507. Maximum output per response: GLM-4.7-FlashX up to 131,072, Qwen Flash up to 32,768, Voxtral Small 24B 2507 up to 32,768 tokens.
Which can read images, PDFs, audio or video?
GLM-4.7-FlashX accepts text; Qwen Flash accepts text; Voxtral Small 24B 2507 accepts text and audio. Voxtral Small 24B 2507 handles the widest range of inputs.
Are any of these open source?
GLM-4.7-FlashX and Voxtral Small 24B 2507 publishes its weights (Apache 2.0) and can be self-hosted; Qwen Flash is proprietary.
Which is newer?
GLM-4.7-FlashX is the newest, released Jan 19, 2026. Qwen Flash came out Jul 28, 2025; Voxtral Small 24B 2507 came out Jul 15, 2025. Knowledge cutoff: GLM-4.7-FlashX Apr 2025, Qwen Flash 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.