Gemini 1.5 Pro vs Llama 4 Scout 17B-16E Instruct
Gemini 1.5 Pro (2024) and Llama 4 Scout 17B-16E Instruct (2025) are general-purpose language models from Google DeepMind and AI at Meta. Gemini 1.5 Pro ships a 2m-token context window, while Llama 4 Scout 17B-16E Instruct ships a 10m-token context window. On MMLU PRO, Llama 4 Scout 17B-16E Instruct leads by 5.3 pts. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Llama 4 Scout 17B-16E Instruct is ~1462% cheaper at $0.08/1M; pay for Gemini 1.5 Pro only for provider fit.
Decision scorecard
Local evidence first| Signal | Gemini 1.5 Pro | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Best for | long-context analysis and provider-routed production | multimodal apps, long-context analysis, and provider-routed production |
| Decision fit | RAG, Long context, and Vision | Coding, RAG, and Agents |
| Context window | 2m | 10m |
| Cheapest output | $5/1M tokens | $0.30/1M tokens |
| Provider routes | 2 tracked | 12 tracked |
| Shared benchmarks | 3 shared | MMLU PRO leader |
Decision tradeoffs
- Local decision data tags Gemini 1.5 Pro for RAG, Long context, and Vision.
- Llama 4 Scout 17B-16E Instruct holds a shared-benchmark lead on MMLU PRO, ahead by 5.3 points.
- Llama 4 Scout 17B-16E Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 4 Scout 17B-16E Instruct has the lower cheapest tracked output price at $0.30/1M tokens.
- Llama 4 Scout 17B-16E Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 4 Scout 17B-16E Instruct uniquely exposes Vision and Multimodal in local model data.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Gemini 1.5 Pro
$2,250
Cheapest tracked route/tier: GCP Vertex AI
Llama 4 Scout 17B-16E Instruct
$139
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $2,111. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on GCP Vertex AI; start route-level A/B tests there.
- Llama 4 Scout 17B-16E Instruct is $4.70/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Llama 4 Scout 17B-16E Instruct adds Vision and Multimodal in local capability data.
- Provider overlap exists on GCP Vertex AI; start route-level A/B tests there.
- Gemini 1.5 Pro is $4.70/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Vision and Multimodal before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-02-15 | 2025-04-05 |
| Context window | 2m | 10m |
| Parameters | — | 109B (17B active) |
| Architecture | Decoder Only | Mixture of Experts |
| License | Proprietary | Llama 4 Community |
| Openness | Proprietary | Open weights |
| Commercial use | Commercial use: conditional | Commercial use: conditional |
| Knowledge cutoff | 2023-11 | 2024-08 |
Pricing and availability
| Pricing attribute | Gemini 1.5 Pro | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Input price | $1.25/1M tokens | $0.08/1M tokens |
| Output price | $5/1M tokens | $0.30/1M tokens |
| Providers |
Capabilities
| Capability | Gemini 1.5 Pro | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Gemini 1.5 Pro | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| MMLU PRO | 69.0 | 74.3 |
| Massive Multi-discipline Multimodal Understanding | 65.8 | 69.4 |
| Chatbot Arena | 1220.0 | 1295.0 |
Deep dive
On shared benchmark coverage, MMLU PRO has Gemini 1.5 Pro at 69.0 and Llama 4 Scout 17B-16E Instruct at 74.3, with Llama 4 Scout 17B-16E Instruct ahead by 5.3 points; Massive Multi-discipline Multimodal Understanding has Gemini 1.5 Pro at 65.8 and Llama 4 Scout 17B-16E Instruct at 69.4, with Llama 4 Scout 17B-16E Instruct ahead by 3.6 points; Chatbot Arena has Gemini 1.5 Pro at 1220 and Llama 4 Scout 17B-16E Instruct at 1295, with Llama 4 Scout 17B-16E Instruct ahead by 75 points. The largest visible gap is 75 points on Chatbot Arena, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on vision: Llama 4 Scout 17B-16E Instruct and multimodal input: Llama 4 Scout 17B-16E Instruct. Both models share structured outputs, 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.
For cost, Gemini 1.5 Pro lists $1.25/1M input and $5/1M output tokens on the cheapest tracked provider, while Llama 4 Scout 17B-16E Instruct lists $0.08/1M input and $0.30/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 4 Scout 17B-16E Instruct lower by about $2.23 per million blended tokens. Availability is 2 providers versus 12, so concentration risk also matters.
Choose Gemini 1.5 Pro when provider fit are central to the workload. Choose Llama 4 Scout 17B-16E Instruct when long-context analysis, larger context windows, and lower input-token cost are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, Gemini 1.5 Pro or Llama 4 Scout 17B-16E Instruct?
Llama 4 Scout 17B-16E Instruct supports 10m tokens, while Gemini 1.5 Pro supports 2m tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, Gemini 1.5 Pro or Llama 4 Scout 17B-16E Instruct?
Llama 4 Scout 17B-16E Instruct is cheaper on tracked token pricing. Gemini 1.5 Pro costs $1.25/1M input and $5/1M output tokens. Llama 4 Scout 17B-16E Instruct costs $0.08/1M input and $0.30/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Gemini 1.5 Pro or Llama 4 Scout 17B-16E Instruct open source?
Gemini 1.5 Pro is listed under Proprietary. Llama 4 Scout 17B-16E Instruct is listed under Llama 4 Community. 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 1.5 Pro or Llama 4 Scout 17B-16E Instruct?
Llama 4 Scout 17B-16E Instruct 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 1.5 Pro or Llama 4 Scout 17B-16E Instruct?
Llama 4 Scout 17B-16E Instruct 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.
Where can I run Gemini 1.5 Pro and Llama 4 Scout 17B-16E Instruct?
Gemini 1.5 Pro is available on GCP Vertex AI and Google AI Studio. Llama 4 Scout 17B-16E Instruct is available on Cloudflare Workers AI, OpenRouter, Together AI, Fireworks AI, and DeepInfra. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-06-15. Data sourced from public model cards and provider documentation.