Gemini Deep Research vs Llama 3.1 70B Instruct
At a Glance
| Compare |  Gemini Deep ResearchGoogle DeepMind | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.40Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $0.40Openrouter ↗ · 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 | Llama-3.1-70B-Instruct |
|---|---|---|
| Developer | Google DeepMind | Meta |
| Family | Gemini Agents | Llama 3 1 70b Instruct |
| Model | Gemini Deep Research | Llama-3.1-70B-Instruct |
| Version | Gemini Deep Research | Llama-3.1-70B-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 | 70.6B |
| 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) | Openrouter (Standard) |
| Capabilities | generation, reasoning, research, tools | chat, generation, tools |
Gemini Deep Research Capabilities
Llama 3.1 70B Instruct Capabilities
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Primary Evidence
Sources and Freshness
Questions
Gemini Deep Research vs Llama 3.1 70B Instruct FAQs
Is Gemini Deep Research or Llama 3.1 70B Instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini Deep Research and Llama 3.1 70B Instruct, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemini Deep Research or Llama 3.1 70B Instruct?+
Only Llama 3.1 70B Instruct has a directly sourced input price: $0.40 per million tokens. Only Llama 3.1 70B Instruct has a directly sourced output price: $0.40 per million tokens.
Which has a larger context window, Gemini Deep Research or Llama 3.1 70B Instruct?+
Gemini Deep Research has the larger sourced context window. Gemini Deep Research supports 1,049K and Llama 3.1 70B Instruct supports 131K.
Which performs better in benchmarks, Gemini Deep Research or Llama 3.1 70B 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 or Llama 3.1 70B Instruct be self-hosted?+
Llama 3.1 70B Instruct is the only model in this pair currently marked as self-hostable. Gemini Deep Research is not marked open weight; Llama 3.1 70B Instruct is open weight.
Can Gemini Deep Research and Llama 3.1 70B Instruct understand images?+
Gemini Deep Research is documented with image input; Llama 3.1 70B Instruct is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemini Deep Research or Llama 3.1 70B Instruct?+
Neither has a larger sourced maximum output. Gemini Deep Research is 66K and Llama 3.1 70B Instruct is —.
Do Gemini Deep Research and Llama 3.1 70B Instruct support reasoning and tool use?+
Gemini Deep Research: reasoning, tool calling, and image input. Llama 3.1 70B Instruct: tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Gemini Deep Research or Llama 3.1 70B Instruct?+
Gemini Deep Research has 2 sourced provider routes; Llama 3.1 70B Instruct has 1, so Gemini Deep Research has broader tracked availability.
Which offers better value, Gemini Deep Research or Llama 3.1 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.