Gemini Deep Research Max vs Llama 3.1 8B Instruct
At a Glance
| Compare |  Gemini Deep Research MaxGoogle DeepMind | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.050Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $0.080Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 1,049K | 131K |
| Model facts checked | Aug 29, 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 | Gemini Deep Research Max | Llama-3.1-8B-Instruct |
|---|---|---|
| Developer | Google DeepMind | Meta |
| Family | Gemini Agents | Llama 3 1 8b Instruct |
| Model | Gemini Deep Research Max | Llama-3.1-8B-Instruct |
| Version | Gemini Deep Research Max | Llama-3.1-8B-Instruct |
| Lifecycle | preview | active |
| Released | Unknown | 2024-07-23 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Video, Audio, Document | Text |
| Output modalities | Text, Image | Text |
| Context window | 1,049K | 131K |
| Total parameters | Unknown | 8B |
| Active parameters | Unknown | Unknown |
| License | Unknown | llama3.1 |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Google AI (Standard), Google Gemini (Standard) | Hugging Face (Standard), Openrouter (Standard) |
| Capabilities | generation, reasoning, research, tools | chat, generation, tools |
Gemini Deep Research Max Capabilities
Llama 3.1 8B Instruct Capabilities
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Primary Evidence
Sources and Freshness
Questions
Gemini Deep Research Max vs Llama 3.1 8B Instruct FAQs
Is Gemini Deep Research Max or Llama 3.1 8B Instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini Deep Research Max and Llama 3.1 8B Instruct, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemini Deep Research Max or Llama 3.1 8B Instruct?+
Only Llama 3.1 8B Instruct has a directly sourced input price: $0.050 per million tokens. Only Llama 3.1 8B Instruct has a directly sourced output price: $0.080 per million tokens.
Which has a larger context window, Gemini Deep Research Max or Llama 3.1 8B Instruct?+
Gemini Deep Research Max has the larger sourced context window. Gemini Deep Research Max supports 1,049K and Llama 3.1 8B Instruct supports 131K.
Which performs better in benchmarks, Gemini Deep Research Max or Llama 3.1 8B Instruct?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Gemini Deep Research Max or Llama 3.1 8B Instruct be self-hosted?+
Llama 3.1 8B Instruct is the only model in this pair currently marked as self-hostable. Gemini Deep Research Max is not marked open weight; Llama 3.1 8B Instruct is open weight.
Can Gemini Deep Research Max and Llama 3.1 8B Instruct understand images?+
Gemini Deep Research Max is documented with image input; Llama 3.1 8B Instruct is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemini Deep Research Max or Llama 3.1 8B Instruct?+
Neither has a larger sourced maximum output. Gemini Deep Research Max is 66K and Llama 3.1 8B Instruct is —.
Do Gemini Deep Research Max and Llama 3.1 8B Instruct support reasoning and tool use?+
Gemini Deep Research Max: reasoning, tool calling, and image input. Llama 3.1 8B Instruct: tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Gemini Deep Research Max or Llama 3.1 8B Instruct?+
Gemini Deep Research Max has 2 sourced provider routes; Llama 3.1 8B Instruct has 2, a tie.
Which offers better value, Gemini Deep Research Max or Llama 3.1 8B 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.