Gemini 2.5 Flash vs Llama 4 Maverick 17B Instruct FP8
Gemini 2.5 Flash (2025) and Llama 4 Maverick 17B Instruct FP8 (2025) are general-purpose language models from Google DeepMind and AI at Meta. Gemini 2.5 Flash ships a 1m-token context window, while Llama 4 Maverick 17B Instruct FP8 ships a 1m-token context window. On MMLU PRO, Gemini 2.5 Flash leads by 7.9 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 ~100% cheaper at $0.15/1M; pay for Gemini 2.5 Flash only for coding workflow support.
Decision scorecard
Local evidence first| Signal | Gemini 2.5 Flash | Llama 4 Maverick 17B Instruct FP8 |
|---|---|---|
| Best for | multimodal apps, tool-calling agents, and long-context analysis | multimodal apps, long-context analysis, and provider-routed production |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 1m | 1m |
| Cheapest output | $2.50/1M tokens | $0.60/1M tokens |
| Provider routes | 5 tracked | 10 tracked |
| Shared benchmarks | MMLU PRO leader | 7 shared |
Decision tradeoffs
- Gemini 2.5 Flash holds a shared-benchmark lead on MMLU PRO, ahead by 7.9 points.
- Gemini 2.5 Flash uniquely exposes Function calling, Tool use, and Code execution in local model data.
- Local decision data tags Gemini 2.5 Flash for Coding, RAG, and Agents.
- Llama 4 Maverick 17B Instruct FP8 holds a shared-benchmark lead on Chatbot Arena, ahead by 45 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.
- 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 2.5 Flash
$865
Cheapest tracked route/tier: Google AI Studio
Llama 4 Maverick 17B Instruct FP8
$270
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $595. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter, GCP Vertex AI, and Vercel AI Gateway; start route-level A/B tests there.
- Llama 4 Maverick 17B Instruct FP8 is $1.90/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Function calling, Tool use, and Code execution before moving production traffic.
- Provider overlap exists on GCP Vertex AI, OpenRouter, and Vercel AI Gateway; start route-level A/B tests there.
- Gemini 2.5 Flash is $1.90/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Gemini 2.5 Flash adds Function calling, Tool use, and Code execution in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-06-17 | 2025-04-05 |
| Context window | 1m | 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 | 2025-01 | 2024-08 |
Pricing and availability
| Pricing attribute | Gemini 2.5 Flash | Llama 4 Maverick 17B Instruct FP8 |
|---|---|---|
| Input price | $0.30/1M tokens | $0.15/1M tokens |
| Output price | $2.50/1M tokens | $0.60/1M tokens |
| Providers |
Capabilities
| Capability | Gemini 2.5 Flash | Llama 4 Maverick 17B Instruct FP8 |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | No | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | Yes |
| Code execution | Yes | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Gemini 2.5 Flash | Llama 4 Maverick 17B Instruct FP8 |
|---|---|---|
| MMLU PRO | 88.4 | 80.5 |
| Google-Proof Q&A | 82.8 | 67.1 |
| LiveCodeBench | 76.2 | 43.4 |
| Massive Multi-discipline Multimodal Understanding | 79.7 | 73.4 |
| HumanEval | 90.1 | 77.4 |
| Chatbot Arena | 1320.0 | 1365.0 |
| Aider Polyglot | 55.1 | 15.6 |
Deep dive
On shared benchmark coverage, MMLU PRO has Gemini 2.5 Flash at 88.4 and Llama 4 Maverick 17B Instruct FP8 at 80.5, with Gemini 2.5 Flash ahead by 7.9 points; Google-Proof Q&A has Gemini 2.5 Flash at 82.8 and Llama 4 Maverick 17B Instruct FP8 at 67.1, with Gemini 2.5 Flash ahead by 15.7 points; LiveCodeBench has Gemini 2.5 Flash at 76.2 and Llama 4 Maverick 17B Instruct FP8 at 43.4, with Gemini 2.5 Flash ahead by 32.8 points. The largest visible gap is 32.8 points on LiveCodeBench, 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 function calling: Gemini 2.5 Flash, tool use: Gemini 2.5 Flash, and code execution: Gemini 2.5 Flash. Both models share vision, multimodal input, and 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 2.5 Flash lists $0.30/1M input and $2.50/1M 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 $0.68 per million blended tokens. Availability is 5 providers versus 10, so concentration risk also matters.
Choose Gemini 2.5 Flash when coding workflow support 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 2.5 Flash or Llama 4 Maverick 17B Instruct FP8?
Gemini 2.5 Flash supports 1m 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 2.5 Flash or Llama 4 Maverick 17B Instruct FP8?
Llama 4 Maverick 17B Instruct FP8 is cheaper on tracked token pricing. Gemini 2.5 Flash costs $0.30/1M input and $2.50/1M 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 2.5 Flash or Llama 4 Maverick 17B Instruct FP8 open source?
Gemini 2.5 Flash 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 2.5 Flash or Llama 4 Maverick 17B Instruct FP8?
Both Gemini 2.5 Flash and Llama 4 Maverick 17B Instruct FP8 expose vision. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Which is better for multimodal input, Gemini 2.5 Flash or Llama 4 Maverick 17B Instruct FP8?
Both Gemini 2.5 Flash and Llama 4 Maverick 17B Instruct FP8 expose multimodal input. 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 2.5 Flash and Llama 4 Maverick 17B Instruct FP8?
Gemini 2.5 Flash is available on Google AI Studio, GCP Vertex AI, Replicate API, OpenRouter, and Vercel AI Gateway. 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.