Qwen3.8 2.4T A95B vs Llama 4 Maverick 17B 128E
At a Glance
| Compare | ||
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $2.00Deepinfra ↗ · Sep 23, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $6.00Deepinfra ↗ · Sep 23, 2026 | Not reported |
| Context windowMaximum documented tokens | 262K | 1,000K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 28, 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 | Qwen3.8-2.4T-A95B | Llama-4-Maverick-17B-128E |
|---|---|---|
| Developer | Qwen | Meta |
| Family | Qwen3 8 2 4t A95b | Llama 4 Maverick 17b 128e |
| Model | Qwen3.8-2.4T-A95B | Llama-4-Maverick-17B-128E |
| Version | Qwen3.8-2.4T-A95B | Llama-4-Maverick-17B-128E |
| Lifecycle | active | active |
| Released | Unknown | 2025-04-05 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image |
| Output modalities | Text | Text |
| Context window | 262K | 1,000K |
| Total parameters | 2.4T | 401.6B |
| Active parameters | 95B | 17B |
| License | other | other |
| Open weights | Yes | Yes |
| API available | Yes | Unknown |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Unknown |
| Capabilities | chat, generation, reasoning, tools | chat, generation, tools |
Qwen3.8 2.4T A95B Capabilities
Llama 4 Maverick 17B 128E Capabilities
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| vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Qwen3.8 2.4T A95B vs Llama 4 Maverick 17B 128E FAQs
Is Qwen3.8 2.4T A95B or Llama 4 Maverick 17B 128E better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.8 2.4T A95B and Llama 4 Maverick 17B 128E, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Qwen3.8 2.4T A95B or Llama 4 Maverick 17B 128E?+
Only Qwen3.8 2.4T A95B has a directly sourced input price: $2.00 per million tokens. Only Qwen3.8 2.4T A95B has a directly sourced output price: $6.00 per million tokens.
Which has a larger context window, Qwen3.8 2.4T A95B or Llama 4 Maverick 17B 128E?+
Llama 4 Maverick 17B 128E has the larger sourced context window. Qwen3.8 2.4T A95B supports 262K and Llama 4 Maverick 17B 128E supports 1,000K.
Which performs better in benchmarks, Qwen3.8 2.4T A95B or Llama 4 Maverick 17B 128E?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Qwen3.8 2.4T A95B or Llama 4 Maverick 17B 128E be self-hosted?+
Both models have the same recorded self-hosting status: supported. Qwen3.8 2.4T A95B is open weight; Llama 4 Maverick 17B 128E is open weight.
Can Qwen3.8 2.4T A95B and Llama 4 Maverick 17B 128E understand images?+
Qwen3.8 2.4T A95B is not documented with image input; Llama 4 Maverick 17B 128E is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Qwen3.8 2.4T A95B or Llama 4 Maverick 17B 128E?+
Neither has a larger sourced maximum output. Qwen3.8 2.4T A95B is — and Llama 4 Maverick 17B 128E is —.
Do Qwen3.8 2.4T A95B and Llama 4 Maverick 17B 128E support reasoning and tool use?+
Qwen3.8 2.4T A95B: reasoning and tool calling. Llama 4 Maverick 17B 128E: tool calling and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Qwen3.8 2.4T A95B or Llama 4 Maverick 17B 128E?+
Qwen3.8 2.4T A95B has 5 sourced provider routes; Llama 4 Maverick 17B 128E has 0, so Qwen3.8 2.4T A95B has broader tracked availability.
Which offers better value, Qwen3.8 2.4T A95B or Llama 4 Maverick 17B 128E?+
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.