Gemini 1.5 Pro vs Llama 4 Maverick 17B Instruct FP8
Gemini 1.5 Pro (2024) and Llama 4 Maverick 17B Instruct FP8 (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 Maverick 17B Instruct FP8 ships a 1m-token context window. On MMLU PRO, Llama 4 Maverick 17B Instruct FP8 leads by 11.5 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 Maverick 17B Instruct FP8 is ~733% cheaper at $0.15/1M; pay for Gemini 1.5 Pro only for long-context analysis.
Decision scorecard
Local evidence first| Signal | Gemini 1.5 Pro | Llama 4 Maverick 17B Instruct FP8 |
|---|---|---|
| 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 | 1m |
| Cheapest output | $5/1M tokens | $0.60/1M tokens |
| Provider routes | 2 tracked | 10 tracked |
| Shared benchmarks | 4 shared | MMLU PRO leader |
Decision tradeoffs
- Gemini 1.5 Pro holds a shared-benchmark lead on MMMU Pro, ahead by 1 points.
- Gemini 1.5 Pro has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Gemini 1.5 Pro for RAG, Long context, and Vision.
- Llama 4 Maverick 17B Instruct FP8 holds a shared-benchmark lead on MMLU PRO, ahead by 11.5 points.
- Llama 4 Maverick 17B Instruct FP8 has the lower cheapest tracked output price at $0.60/1M tokens.
- Llama 4 Maverick 17B Instruct FP8 has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 4 Maverick 17B Instruct FP8 uniquely exposes Vision and Multimodal in local model data.
- Local decision data tags Llama 4 Maverick 17B Instruct FP8 for Coding, RAG, and Agents.
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 Maverick 17B Instruct FP8
$270
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $1,980. 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 Maverick 17B Instruct FP8 is $4.40/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Llama 4 Maverick 17B Instruct FP8 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 $4.40/1M 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 | 1m |
| Parameters | — | 400B (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 Maverick 17B Instruct FP8 |
|---|---|---|
| Input price | $1.25/1M tokens | $0.15/1M tokens |
| Output price | $5/1M tokens | $0.60/1M tokens |
| Providers |
Capabilities
| Capability | Gemini 1.5 Pro | Llama 4 Maverick 17B Instruct FP8 |
|---|---|---|
| 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 Maverick 17B Instruct FP8 |
|---|---|---|
| MMLU PRO | 69.0 | 80.5 |
| Massive Multi-discipline Multimodal Understanding | 65.8 | 73.4 |
| Chatbot Arena | 1220.0 | 1365.0 |
| MMMU Pro | 60.6 | 59.6 |
Deep dive
On shared benchmark coverage, MMLU PRO has Gemini 1.5 Pro at 69.0 and Llama 4 Maverick 17B Instruct FP8 at 80.5, with Llama 4 Maverick 17B Instruct FP8 ahead by 11.5 points; Massive Multi-discipline Multimodal Understanding has Gemini 1.5 Pro at 65.8 and Llama 4 Maverick 17B Instruct FP8 at 73.4, with Llama 4 Maverick 17B Instruct FP8 ahead by 7.6 points; Chatbot Arena has Gemini 1.5 Pro at 1220 and Llama 4 Maverick 17B Instruct FP8 at 1365, with Llama 4 Maverick 17B Instruct FP8 ahead by 145 points. The largest visible gap is 145 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 Maverick 17B Instruct FP8 and multimodal input: Llama 4 Maverick 17B Instruct FP8. 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 Maverick 17B Instruct FP8 lists $0.15/1M input and $0.60/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 4 Maverick 17B Instruct FP8 lower by about $2.09 per million blended tokens. Availability is 2 providers versus 10, so concentration risk also matters.
Choose Gemini 1.5 Pro when long-context analysis and larger context windows are central to the workload. Choose Llama 4 Maverick 17B Instruct FP8 when vision-heavy evaluation, lower input-token cost, and broader provider choice 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 Maverick 17B Instruct FP8?
Gemini 1.5 Pro supports 2m tokens, while Llama 4 Maverick 17B Instruct FP8 supports 1m 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 Maverick 17B Instruct FP8?
Llama 4 Maverick 17B Instruct FP8 is cheaper on tracked token pricing. Gemini 1.5 Pro costs $1.25/1M input and $5/1M output tokens. Llama 4 Maverick 17B Instruct FP8 costs $0.15/1M input and $0.60/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Gemini 1.5 Pro or Llama 4 Maverick 17B Instruct FP8 open source?
Gemini 1.5 Pro is listed under Proprietary. Llama 4 Maverick 17B Instruct FP8 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 Maverick 17B Instruct FP8?
Llama 4 Maverick 17B Instruct FP8 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 Maverick 17B Instruct FP8?
Llama 4 Maverick 17B Instruct FP8 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 Maverick 17B Instruct FP8?
Gemini 1.5 Pro is available on GCP Vertex AI and Google AI Studio. Llama 4 Maverick 17B Instruct FP8 is available on Microsoft Foundry, Together AI, OpenRouter, 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.