Qwen3.8 2.4T A95B vs Llama 3.3 70B Instruct
At a Glance
| Compare | ||
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $2.00Deepinfra ↗ · Sep 23, 2026 | $0.10Openrouter ↗ · Sep 23, 2026 |
| Output priceFrom · USD / 1M tokens | $6.00Deepinfra ↗ · Sep 23, 2026 | $0.32Openrouter ↗ · Sep 23, 2026 |
| Context windowMaximum documented tokens | 262K | 131K |
| 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-3.3-70B-Instruct |
|---|---|---|
| Developer | Qwen | Meta |
| Family | Qwen3 8 2 4t A95b | Llama 3 3 70b Instruct |
| Model | Qwen3.8-2.4T-A95B | Llama-3.3-70B-Instruct |
| Version | Qwen3.8-2.4T-A95B | Llama-3.3-70B-Instruct |
| Lifecycle | active | active |
| Released | Unknown | 2024-12-06 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 262K | 131K |
| Total parameters | 2.4T | 70.6B |
| Active parameters | 95B | Unknown |
| License | other | llama3.3 |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, tools |
Qwen3.8 2.4T A95B Capabilities
Llama 3.3 70B Instruct Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
| vs |  GPT-6 AstraOpenAI | cross-developer peerstext | |
| vs | Claude Fable 5.1Anthropic | cross-developer peerstext | |
| vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext | |
| vs |  DeepSeek-V4.1-FlashDeepSeek | cross-developer peerstext | |
| vs | Grok 4.6xAI | cross-developer peerstext | |
| vs |  Qwen3.8-MaxQwen | family variantstext | |
| vs |  Kimi-K3Moonshot AI | cross-developer peerstext | |
 MiniMax-M3MiniMax | vs | cross-developer peerstext | |
| vs |  GLM-5.3Z.ai | cross-developer peerstext | |
| vs |  Hy4 previewTencent | cross-developer peerstext | |
| vs | Seed 2.1 ProByteDance Seed | cross-developer peerstext | |
| vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Qwen3.8 2.4T A95B vs Llama 3.3 70B Instruct FAQs
Is Qwen3.8 2.4T A95B or Llama 3.3 70B Instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.8 2.4T A95B and Llama 3.3 70B Instruct, 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 3.3 70B Instruct?+
Qwen3.8 2.4T A95B is $2.00 and Llama 3.3 70B Instruct is $0.10 per million tokens, so Llama 3.3 70B Instruct is cheaper on this metric. Qwen3.8 2.4T A95B is $6.00 and Llama 3.3 70B Instruct is $0.32 per million tokens, so Llama 3.3 70B Instruct is cheaper on this metric.
Which has a larger context window, Qwen3.8 2.4T A95B or Llama 3.3 70B Instruct?+
Qwen3.8 2.4T A95B has the larger sourced context window. Qwen3.8 2.4T A95B supports 262K and Llama 3.3 70B Instruct supports 131K.
Which performs better in benchmarks, Qwen3.8 2.4T A95B or Llama 3.3 70B Instruct?+
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 3.3 70B Instruct be self-hosted?+
Both models have the same recorded self-hosting status: supported. Qwen3.8 2.4T A95B is open weight; Llama 3.3 70B Instruct is open weight.
Can Qwen3.8 2.4T A95B and Llama 3.3 70B Instruct understand images?+
Qwen3.8 2.4T A95B is not documented with image input; Llama 3.3 70B Instruct is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Qwen3.8 2.4T A95B or Llama 3.3 70B Instruct?+
Neither has a larger sourced maximum output. Qwen3.8 2.4T A95B is — and Llama 3.3 70B Instruct is —.
Do Qwen3.8 2.4T A95B and Llama 3.3 70B Instruct support reasoning and tool use?+
Qwen3.8 2.4T A95B: reasoning and tool calling. Llama 3.3 70B Instruct: tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Qwen3.8 2.4T A95B or Llama 3.3 70B Instruct?+
Qwen3.8 2.4T A95B has 5 sourced provider routes; Llama 3.3 70B Instruct has 3, so Qwen3.8 2.4T A95B has broader tracked availability.
Which offers better value, Qwen3.8 2.4T A95B or Llama 3.3 70B Instruct?+
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.