Gemini 3.8 Flash Cyber vs Qwen3.7 Max
At a Glance
| Compare | Gemini 3.8 Flash CyberGoogle DeepMind | Qwen3.7 MaxQwen |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #13 of 44$0.057 per LiveBench case |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $1.475Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $4.425Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | Not reported | 1,000K |
| Model facts checked | Sep 2, 2026View model evidence → | Sep 3, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
Side-by-Side Facts
| Field | Gemini 3.8 Flash Cyber | Qwen3.7 Max |
|---|---|---|
| Developer | Google DeepMind | Qwen |
| Family | Gemini 3 | Qwen3 7 |
| Model | Gemini 3.8 Flash Cyber | Qwen3.7 Max |
| Version | Gemini 3.8 Flash Cyber | Qwen3.7 Max |
| Lifecycle | active | active |
| Released | 2026-09-02 | 2026-05-20 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image, Video |
| Output modalities | Text | Text |
| Context window | Unknown | 1,000K |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | No | Yes |
| Self-hostable | No | No |
| Provider access | Unknown | Alibaba Cloud Model Studio (Standard), Deepinfra (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | automated-patching, cybersecurity, reasoning, vulnerability-detection | agents, chat, generation, reasoning, structured_outputs, tools, vision |
Gemini 3.8 Flash Cyber Capabilities
Qwen3.7 Max Capabilities
Primary Evidence
Sources and Freshness
Questions
Gemini 3.8 Flash Cyber vs Qwen3.7 Max FAQs
Is Gemini 3.8 Flash Cyber or Qwen3.7 Max better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.8 Flash Cyber and Qwen3.7 Max, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemini 3.8 Flash Cyber or Qwen3.7 Max?+
Only Qwen3.7 Max has a directly sourced input price: $1.475 per million tokens. Only Qwen3.7 Max has a directly sourced output price: $4.425 per million tokens.
Which has a larger context window, Gemini 3.8 Flash Cyber or Qwen3.7 Max?+
Neither model has a larger sourced context window in this comparison. Gemini 3.8 Flash Cyber is — and Qwen3.7 Max is 1,000K.
Which performs better in benchmarks, Gemini 3.8 Flash Cyber or Qwen3.7 Max?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Gemini 3.8 Flash Cyber or Qwen3.7 Max be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Gemini 3.8 Flash Cyber is not marked open weight; Qwen3.7 Max is not marked open weight.
Can Gemini 3.8 Flash Cyber and Qwen3.7 Max understand images?+
Gemini 3.8 Flash Cyber is not documented with image input; Qwen3.7 Max is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemini 3.8 Flash Cyber or Qwen3.7 Max?+
Neither has a larger sourced maximum output. Gemini 3.8 Flash Cyber is — and Qwen3.7 Max is 66K.
Do Gemini 3.8 Flash Cyber and Qwen3.7 Max support reasoning and tool use?+
Gemini 3.8 Flash Cyber: reasoning. Qwen3.7 Max: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Gemini 3.8 Flash Cyber or Qwen3.7 Max?+
Gemini 3.8 Flash Cyber has 0 sourced provider routes; Qwen3.7 Max has 4, so Qwen3.7 Max has broader tracked availability.
Which offers better value, Gemini 3.8 Flash Cyber or Qwen3.7 Max?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.