Qwen3.8 Omni Flash vs Grok 4.7 vs Viv Fast
Qwen3.8 Omni Flash comes out ahead, 79 to 75 and 47 on our weighted score, though Viv Fast is 14% cheaper per token.
- Our pick
Alibaba (Qwen)Qwen3.8 Omni Flash
79/100- ECI—
- Price$0.15 / $0.47
- Context1M
xAIGrok 4.7
47/100- ECI—
- Price$2.00 / $6.00
- Context500K
VivgridViv Fast
75/100- ECI—
- Price$0.13 / $0.40
- Context1M
Qwen3.8 Omni Flash is our pick
Qwen3.8 Omni Flash is the better all-round choice, scoring 79/100 against Viv Fast (75) and Grok 4.7 (47). It leads on inputs & features. Viv Fast 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 priceViv FastViv Fast $0.198 · Qwen3.8 Omni Flash $0.23 · Grok 4.7 $3.00 per 1M tokens (3:1 blend)
- Longest contextQwen3.8 Omni Flash and Viv FastQwen3.8 Omni Flash 1,000,000 · Viv Fast 1,000,000 · Grok 4.7 500,000 tokens
- Widest inputsQwen3.8 Omni FlashQwen3.8 Omni Flash: Text, Images, Audio, Video · Grok 4.7: Text, Images, PDFs · Viv Fast: Text, Images
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | Qwen3.8 Omni Flash | Grok 4.7 | Viv Fast |
|---|---|---|---|---|
| Price | 50% | 80 | 27 | 83 |
| Inputs & features | 30% | 90 | 80 | 70 |
| Context window | 20% | 60 | 48 | 60 |
| Overall | 100% | 79/100 | 47/100 | 75/100 |
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 | Qwen3.8 Omni FlashAlibaba (Qwen) | Grok 4.7xAI | Viv FastVivgrid |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| GPQA DiamondGraduate-level science questions | — | 92.7% | — |
| FrontierMath Tiers 1–3Research-level mathematics | — | 53.0% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 98.1% | — |
| SimpleQA VerifiedShort factual questions | — | 56.0% | — |
| Price per million tokens | |||
| Input | $0.15 | $2.00 | $0.13 (best) |
| Output | $0.47 | $6.00 | $0.40 (best) |
| Cached input | $0.016 (best) | $0.50 | $0.05 |
| Blended (3:1) | $0.23 | $3.00 | $0.198 (best) |
| Long-context rate | Same rate | Over 200K: $4.00 / $12.00 | Same rate |
| Price source | Official Alibaba API | Official xAI API | Official Vivgrid API |
| Limits | |||
| Context window | 1,000,000 tokens (best) | 500,000 tokens | 1,000,000 tokens (best) |
| Max output | 131,072 tokens | 500,000 tokens (best) | 256,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | Yes | No |
| Audio | Yes | No | No |
| Video | Yes | No | No |
| Reasoning | Yeslow · medium · xhigh | Yeslow · medium · high · xhigh | Yeslow · high · max |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | qwen3.8-omni-flash | grok-4.7 | viv-fast |
| API providers | 10 | 17 (best) | 1 |
| Released | Sep 17, 2026 | Sep 21, 2026 | Sep 21, 2026 |
| Knowledge cutoff | — | May 2026 | Sep 2026 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen3.8 Omni Flash$2.44
- Grok 4.7$32.00
- Viv Fast$2.10
Which should you choose?
Which is better: Qwen3.8 Omni Flash, Grok 4.7 or Viv Fast?
Qwen3.8 Omni Flash is the better all-round choice, scoring 79/100 against Viv Fast (75) and Grok 4.7 (47). It leads on inputs & features. Viv Fast 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, Qwen3.8 Omni Flash, Grok 4.7 or Viv Fast?
Viv Fast is cheaper at $0.13 input / $0.40 output per million tokens (official Vivgrid API price). Qwen3.8 Omni Flash costs $0.15 input / $0.47 output per million tokens (official Alibaba API price); Grok 4.7 costs $2.00 input / $6.00 output per million tokens (official xAI API price). At a typical mix of three input tokens to one output token, that is $0.198 per million tokens for Viv Fast versus $0.23 for Qwen3.8 Omni Flash (1.2× as much) and $3.00 for Grok 4.7 (15× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen3.8 Omni Flash has not been scored yet, Grok 4.7 has not been scored yet and Viv Fast has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3.8 Omni Flash, Grok 4.7 and Viv Fast 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?
Qwen3.8 Omni Flash and Viv Fast have the largest context windows (1,000,000 and 1,000,000 tokens), against 500,000 for Grok 4.7. Maximum output per response: Qwen3.8 Omni Flash up to 131,072, Grok 4.7 up to 500,000, Viv Fast up to 256,000 tokens.
Which can read images, PDFs, audio or video?
Qwen3.8 Omni Flash accepts text, images, audio and video; Grok 4.7 accepts text, images and PDFs; Viv Fast accepts text and images. Qwen3.8 Omni Flash handles the widest range of inputs.
Are any of these open source?
No. Qwen3.8 Omni Flash, Grok 4.7 and Viv Fast are proprietary and only available through APIs and apps.
Which is newer?
Grok 4.7 is the newest, released Sep 21, 2026. Viv Fast came out Sep 21, 2026; Qwen3.8 Omni Flash came out Sep 17, 2026. Knowledge cutoff: Grok 4.7 May 2026, Viv Fast Sep 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.
