Gemini 2.5 Pro Vision vs Llama 4 Maverick Instruct: which model should you choose?
We compared Gemini 2.5 Pro Vision and Llama 4 Maverick Instruct on price, context window and 12 benchmarks. Gemini 2.5 Pro Vision has the stronger average benchmark score; Llama 4 Maverick Instruct is cheaper on a blended 3:1 basis.
Gemini 2.5 Pro Vision
Gemini 2.5 Pro Vision is a multimodal model from Google DeepMind. It offers a 200,000-token context window, costs $14.91 per million input tokens and $71.00 per million output tokens, and averages 82.0 across the index's benchmark suite.
- Input
- $14.91 / 1M
- Output
- $71.00 / 1M
- Context
- 200,000
- Avg score
- 82.0
- Open weights
- No
Llama 4 Maverick Instruct
Llama 4 Maverick Instruct is a multimodal model from Meta AI. It offers a 200,000-token context window, costs $5.33 per million input tokens and $23.04 per million output tokens, and averages 81.7 across the index's benchmark suite.
- Input
- $5.33 / 1M
- Output
- $23.04 / 1M
- Context
- 200,000
- Avg score
- 81.7
- Open weights
- Yes
Specification comparison
| Spec | Gemini 2.5 Pro Vision | Llama 4 Maverick Instruct | Edge |
|---|---|---|---|
| Input price / 1M | $14.91 | $5.33 | B |
| Output price / 1M | $71.00 | $23.04 | B |
| Context window | 200,000 | 200,000 | Tie |
| Max output | 4,096 | 16,384 | B |
| Average score | 82.0 | 81.7 | A |
| Arena Elo | 1451.3 | 1438.5 | A |
| Open weights | No | Yes | B |
Benchmark comparison
Gemini 2.5 Pro Vision wins 8, Llama 4 Maverick Instruct wins 4, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | Gemini 2.5 Pro Vision | Llama 4 Maverick Instruct | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 83.9 | 84.6 | 0.7 | Llama 4 Maverick Instruct |
| GPQA Diamond | 78.0 | 79.5 | 1.5 | Llama 4 Maverick Instruct |
| SWE-bench Verified | 72.4 | 69.1 | 3.3 | Gemini 2.5 Pro Vision |
| HumanEval | 85.8 | 85.0 | 0.8 | Gemini 2.5 Pro Vision |
| MATH-500 | 82.9 | 83.4 | 0.5 | Llama 4 Maverick Instruct |
| AIME 2025 | 80.0 | 79.9 | 0.1 | Gemini 2.5 Pro Vision |
| LiveCodeBench | 76.0 | 75.8 | 0.2 | Gemini 2.5 Pro Vision |
| IFEval | 90.2 | 88.9 | 1.3 | Gemini 2.5 Pro Vision |
| TruthfulQA | 84.1 | 84.0 | 0.1 | Gemini 2.5 Pro Vision |
| HellaSwag | 90.8 | 89.6 | 1.2 | Gemini 2.5 Pro Vision |
| MMMU | 78.2 | 79.0 | 0.8 | Llama 4 Maverick Instruct |
| Arena Elo | 1451.3 | 1438.5 | 12.8 | Gemini 2.5 Pro Vision |
Price analysis
On a blended 3:1 input-to-output basis, Gemini 2.5 Pro Vision costs about $28.93 per million tokens and Llama 4 Maverick Instruct costs about $9.76. Llama 4 Maverick Instruct is roughly 66% cheaper than Gemini 2.5 Pro Vision. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Both models list a 200,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, Gemini 2.5 Pro Vision wins 8, Llama 4 Maverick Instruct wins 4, and 0 are tied. On the index average, Gemini 2.5 Pro Vision leads Llama 4 Maverick Instruct by 0.3 points (82.0 versus 81.7).
The price picture favours Llama 4 Maverick Instruct. The context picture is effectively a tie. Those two dimensions often matter more than a few benchmark points once a model is good enough for the task.
If you need a single default, start with Gemini 2.5 Pro Vision for quality-sensitive work and Llama 4 Maverick Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose Gemini 2.5 Pro Vision if
- You want the higher average benchmark score in this index.
Choose Llama 4 Maverick Instruct if
- Unit cost matters and you want the cheaper blended price.
- You need the larger 16,384-token output limit.
- You need open weights for self-hosting or fine-tuning.
FAQ
Is Gemini 2.5 Pro Vision better than Llama 4 Maverick Instruct?
On the index average, Gemini 2.5 Pro Vision leads Llama 4 Maverick Instruct by 0.3 points (82.0 versus 81.7). The better choice depends on price, context and your task. Gemini 2.5 Pro Vision wins 8 benchmarks, Llama 4 Maverick Instruct wins 4.
Which is cheaper, Gemini 2.5 Pro Vision or Llama 4 Maverick Instruct?
Llama 4 Maverick Instruct is cheaper on a blended 3:1 basis at about $9.76 per million tokens, versus $28.93 for Gemini 2.5 Pro Vision.
Which has the larger context window?
Both models list a 200,000-token context window.
Are these models open weights?
Gemini 2.5 Pro Vision is proprietary; Llama 4 Maverick Instruct is open weights.
Which should I choose for production?
If you need a single default, start with Gemini 2.5 Pro Vision for quality-sensitive work and Llama 4 Maverick Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.