Gemma 4 E4B vs Qwen3.8 Flash
At a Glance
| Compare |  Gemma 4 E4BGoogle DeepMind |  Qwen3.8 FlashQwen |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #4 of 44$0.021 per LiveBench case |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.020Deepinfra ↗ · Sep 21, 2026 | $0.113Deepinfra ↗ · Sep 21, 2026 |
| Output priceFrom · USD / 1M tokens | $0.10Deepinfra ↗ · Sep 21, 2026 | $0.382Deepinfra ↗ · Sep 21, 2026 |
| Context windowMaximum documented tokens | 131K | 1,000K |
| Model facts checked | Sep 3, 2026View model evidence → | Aug 29, 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 | Gemma 4 E4B | Qwen3.8-Flash |
|---|---|---|
| Developer | Google DeepMind | Qwen |
| Family | Gemma 4 | Qwen3 8 Flash |
| Model | Gemma 4 E4B | Qwen3.8-Flash |
| Version | Gemma 4 E4B | Qwen3.8-Flash |
| Lifecycle | active | active |
| Released | 2026-03-02 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Video, Audio | Text, Image, Video |
| Output modalities | Text | Text |
| Context window | 131K | 1,000K |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Deepinfra (Standard), Together Ai (Standard) | Alibaba Cloud Model Studio (Standard), Deepinfra (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | chat, generation, reasoning, structured_outputs, tools | agents, chat, computer-use, reasoning, structured_outputs, tools, vision |
Gemma 4 E4B Capabilities
Qwen3.8 Flash Capabilities
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Primary Evidence
Sources and Freshness
Questions
Gemma 4 E4B vs Qwen3.8 Flash FAQs
Is Gemma 4 E4B or Qwen3.8 Flash better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemma 4 E4B and Qwen3.8 Flash, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemma 4 E4B or Qwen3.8 Flash?+
Gemma 4 E4B is $0.020 and Qwen3.8 Flash is $0.113 per million tokens, so Gemma 4 E4B is cheaper on this metric. Gemma 4 E4B is $0.10 and Qwen3.8 Flash is $0.382 per million tokens, so Gemma 4 E4B is cheaper on this metric.
Which has a larger context window, Gemma 4 E4B or Qwen3.8 Flash?+
Qwen3.8 Flash has the larger sourced context window. Gemma 4 E4B supports 131K and Qwen3.8 Flash supports 1,000K.
Which performs better in benchmarks, Gemma 4 E4B or Qwen3.8 Flash?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Gemma 4 E4B or Qwen3.8 Flash be self-hosted?+
Gemma 4 E4B is the only model in this pair currently marked as self-hostable. Gemma 4 E4B is open weight; Qwen3.8 Flash is not marked open weight.
Can Gemma 4 E4B and Qwen3.8 Flash understand images?+
Gemma 4 E4B is documented with image input; Qwen3.8 Flash is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemma 4 E4B or Qwen3.8 Flash?+
Neither has a larger sourced maximum output. Gemma 4 E4B is — and Qwen3.8 Flash is 131K.
Do Gemma 4 E4B and Qwen3.8 Flash support reasoning and tool use?+
Gemma 4 E4B: reasoning, tool calling, and image input. Qwen3.8 Flash: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Gemma 4 E4B or Qwen3.8 Flash?+
Gemma 4 E4B has 2 sourced provider routes; Qwen3.8 Flash has 4, so Qwen3.8 Flash has broader tracked availability.
Which offers better value, Gemma 4 E4B or Qwen3.8 Flash?+
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.