Gemini Deep Research vs Llama 3.3 70B
Gemini Deep Research (2024) and Llama 3.3 70B (2025) are compact production models from Google DeepMind and AI at Meta. Gemini Deep Research ships a 128K-token context window, while Llama 3.3 70B ships a 8K-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Gemini Deep Research fits 16x more tokens; pick it for long-context work and Llama 3.3 70B for tighter calls.
Decision scorecard
Local evidence first| Signal | Gemini Deep Research | Llama 3.3 70B |
|---|---|---|
| Decision fit | RAG, Agents, and Long context | Agents, Vision, and Classification |
| Context window | 128K | 8K |
| Cheapest output | - | $0.9/1M tokens |
| Provider routes | 1 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Gemini Deep Research has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Gemini Deep Research uniquely exposes Structured outputs in local model data.
- Local decision data tags Gemini Deep Research for RAG, Agents, and Long context.
- Llama 3.3 70B uniquely exposes Vision and Multimodal in local model data.
- Local decision data tags Llama 3.3 70B for Agents, Vision, and Classification.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Gemini Deep Research
Unavailable
No complete token price in local provider data
Llama 3.3 70B
$945
Cheapest tracked route: Fireworks AI
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Gemini Deep Research and Llama 3.3 70B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
- Llama 3.3 70B adds Vision and Multimodal in local capability data.
- No overlapping tracked provider route is sourced for Llama 3.3 70B and Gemini Deep Research; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision and Multimodal before moving production traffic.
- Gemini Deep Research adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-12-11 | 2025-12-09 |
| Context window | 128K | 8K |
| Parameters | — | 70B |
| Architecture | decoder only | decoder only |
| License | Proprietary | True |
| Knowledge cutoff | 2025-01 | 2024-12 |
Pricing and availability
| Pricing attribute | Gemini Deep Research | Llama 3.3 70B |
|---|---|---|
| Input price | - | $0.9/1M tokens |
| Output price | - | $0.9/1M tokens |
| Providers |
Capabilities
| Capability | Gemini Deep Research | Llama 3.3 70B |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | Yes | No |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: Llama 3.3 70B, multimodal input: Llama 3.3 70B, and structured outputs: Gemini Deep Research. Both models share function calling and tool use, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
Pricing coverage is uneven: Gemini Deep Research has no token price sourced yet and Llama 3.3 70B has $0.9/1M input tokens. Provider availability is 1 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Gemini Deep Research when long-context analysis and larger context windows are central to the workload. Choose Llama 3.3 70B when vision-heavy evaluation are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.
FAQ
Which has a larger context window, Gemini Deep Research or Llama 3.3 70B?
Gemini Deep Research supports 128K tokens, while Llama 3.3 70B supports 8K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Gemini Deep Research or Llama 3.3 70B open source?
Gemini Deep Research is listed under Proprietary. Llama 3.3 70B is listed under True. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for vision, Gemini Deep Research or Llama 3.3 70B?
Llama 3.3 70B has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for multimodal input, Gemini Deep Research or Llama 3.3 70B?
Llama 3.3 70B has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for function calling, Gemini Deep Research or Llama 3.3 70B?
Both Gemini Deep Research and Llama 3.3 70B expose function calling. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Where can I run Gemini Deep Research and Llama 3.3 70B?
Gemini Deep Research is available on Google AI Studio. Llama 3.3 70B is available on Fireworks AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.