DeepSeek V4 Flash Vision Exp vs GPT-6 Luna vs Qwen3.8 Flash Next
GPT-6 Luna comes out ahead, 78 to 70 and 70 on our weighted score, and it is the cheaper option too.
DeepSeekDeepSeek V4 Flash Vision Exp
70/100- ECI—
- Price$0.216 / $0.647
- Context1M
- Our pick
OpenAIGPT-6 Luna
78/100- ECI—
- Price$0.10 / $0.50
- Context1.05M
Alibaba (Qwen)Qwen3.8 Flash Next
70/100- ECI—
- Price$0.20 / $0.50
- Context262K
GPT-6 Luna is our pick
GPT-6 Luna is the better all-round choice, scoring 78/100 against DeepSeek V4 Flash Vision Exp (70) and Qwen3.8 Flash Next (70). It leads 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 priceGPT-6 LunaGPT-6 Luna $0.20 · Qwen3.8 Flash Next $0.275 · DeepSeek V4 Flash Vision Exp $0.323 per 1M tokens (3:1 blend)
- Longest contextGPT-6 LunaGPT-6 Luna 1,050,000 · DeepSeek V4 Flash Vision Exp 1,000,000 · Qwen3.8 Flash Next 262,144 tokens
- Widest inputsGPT-6 Luna and Qwen3.8 Flash NextDeepSeek V4 Flash Vision Exp: Text, Images · GPT-6 Luna: Text, Images, PDFs · Qwen3.8 Flash Next: Text, Images, Video
- Self-hostingDeepSeek V4 Flash Vision Exp and Qwen3.8 Flash NextPublishes downloadable weights (MIT and qwen-community-1.0)
| Measure | Weight | DeepSeek V4 Flash Vision Exp | GPT-6 Luna | Qwen3.8 Flash Next |
|---|---|---|---|---|
| Price | 50% | 73 | 83 | 76 |
| Inputs & features | 30% | 70 | 80 | 80 |
| Context window | 20% | 60 | 61 | 37 |
| Overall | 100% | 70/100 | 78/100 | 70/100 |
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 Flash Vision ExpDeepSeek | GPT-6 LunaOpenAI | Qwen3.8 Flash NextAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| GPQA DiamondGraduate-level science questions | — | 90.5% | — |
| FrontierMath Tiers 1–3Research-level mathematics | — | 79.0% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 98.9% | — |
| SimpleQA VerifiedShort factual questions | — | 41.4% | — |
| Price per million tokens | |||
| Input | $0.216 | $0.10 (best) | $0.20 |
| Output | $0.647 | $0.50 (best) | $0.50 (best) |
| Cached input | — | $0.01 | — |
| Blended (3:1) | $0.323 | $0.20 (best) | $0.275 |
| Long-context rate | Same rate | Over 272K: $0.20 / $0.75 | Same rate |
| Price source | Median of 15 providers | Official OpenAI API | Median of 5 providers |
| Limits | |||
| Context window | 1,000,000 tokens | 1,050,000 tokens (best) | 262,144 tokens |
| Max output | 384,000 tokens (best) | 128,000 tokens | 131,072 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | Yes | Yeslow · medium · high · xhigh · max | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | OpenMIT | Proprietary | Openqwen-community-1.0 |
| API model ID | — | gpt-6-luna | — |
| API providers | 15 | 24 (best) | 5 |
| Released | Aug 21, 2026 | Sep 22, 2026 | Aug 27, 2026 |
| Knowledge cutoff | — | May 18, 2026 | — |
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 Flash Vision Exp$3.45
- GPT-6 Luna$2.00
- Qwen3.8 Flash Next$3.00
Which should you choose?
Which is better: DeepSeek V4 Flash Vision Exp, GPT-6 Luna or Qwen3.8 Flash Next?
GPT-6 Luna is the better all-round choice, scoring 78/100 against DeepSeek V4 Flash Vision Exp (70) and Qwen3.8 Flash Next (70). It leads 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 Flash Vision Exp, GPT-6 Luna or Qwen3.8 Flash Next?
GPT-6 Luna is cheaper at $0.10 input / $0.50 output per million tokens (official OpenAI API price). Qwen3.8 Flash Next costs $0.20 input / $0.50 output per million tokens (median across 5 API providers); DeepSeek V4 Flash Vision Exp costs $0.216 input / $0.647 output per million tokens (median across 15 API providers). At a typical mix of three input tokens to one output token, that is $0.20 per million tokens for GPT-6 Luna versus $0.275 for Qwen3.8 Flash Next (1.4× as much) and $0.323 for DeepSeek V4 Flash Vision Exp (1.6× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. DeepSeek V4 Flash Vision Exp has not been scored yet, GPT-6 Luna has not been scored yet and Qwen3.8 Flash Next has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek V4 Flash Vision Exp, GPT-6 Luna and Qwen3.8 Flash Next 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?
GPT-6 Luna has the largest context window at 1,050,000 tokens, against 1,000,000 for DeepSeek V4 Flash Vision Exp and 262,144 for Qwen3.8 Flash Next. Maximum output per response: DeepSeek V4 Flash Vision Exp up to 384,000, GPT-6 Luna up to 128,000, Qwen3.8 Flash Next up to 131,072 tokens.
Which can read images, PDFs, audio or video?
DeepSeek V4 Flash Vision Exp accepts text and images; GPT-6 Luna accepts text, images and PDFs; Qwen3.8 Flash Next accepts text, images and video. GPT-6 Luna handles the widest range of inputs.
Are any of these open source?
DeepSeek V4 Flash Vision Exp and Qwen3.8 Flash Next publishes its weights (MIT and qwen-community-1.0) and can be self-hosted; GPT-6 Luna is proprietary.
Which is newer?
GPT-6 Luna is the newest, released Sep 22, 2026. Qwen3.8 Flash Next came out Aug 27, 2026; DeepSeek V4 Flash Vision Exp came out Aug 21, 2026. Knowledge cutoff: GPT-6 Luna May 18, 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.
