Llama 4 Maverick 17B Instruct vs Gemini 2.5 Flash-Lite Preview 06-17
Gemini 2.5 Flash-Lite Preview 06-17 comes out ahead, 67 to 57 on our weighted score, and it is the cheaper option too.
MetaLlama 4 Maverick 17B Instruct
57/100- ECI132.3
- Price$0.245 / $0.838
- Context1M
- Our pick
GoogleGemini 2.5 Flash-Lite Preview 06-17
67/100- ECI133.8
- Price$0.12 / $0.48
- Context1.05M
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Make it a three-way comparison.
Gemini 2.5 Flash-Lite Preview 06-17 is our pick
Gemini 2.5 Flash-Lite Preview 06-17 is the better all-round choice, scoring 67/100 against Llama 4 Maverick 17B Instruct (57). It leads on capability, price and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGemini 2.5 Flash-Lite Preview 06-17Capabilities Index (ECI): Gemini 2.5 Flash-Lite Preview 06-17 133.8 · Llama 4 Maverick 17B Instruct 132.3
- Lowest priceGemini 2.5 Flash-Lite Preview 06-17Gemini 2.5 Flash-Lite Preview 06-17 $0.21 · Llama 4 Maverick 17B Instruct $0.393 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 Flash-Lite Preview 06-17Gemini 2.5 Flash-Lite Preview 06-17 1,048,576 · Llama 4 Maverick 17B Instruct 1,000,000 tokens
- Widest inputsGemini 2.5 Flash-Lite Preview 06-17Llama 4 Maverick 17B Instruct: Text, Images · Gemini 2.5 Flash-Lite Preview 06-17: Text, Images, PDFs, Audio, Video
- Self-hostingLlama 4 Maverick 17B InstructPublishes downloadable weights
| Measure | Weight | Llama 4 Maverick 17B Instruct | Gemini 2.5 Flash-Lite Preview 06-17 |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 55 | 57 |
| Price | 25% | 66 | 79 |
| Inputs & features | 15% | 50 | 90 |
| Context window | 10% | 60 | 61 |
| Overall | 100% | 57/100 | 67/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Llama 4 Maverick 17B InstructMeta | Gemini 2.5 Flash-Lite Preview 06-17Google |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 132.3 | 133.8 (best) |
| ECI rank | #135 of 165 | #132 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 67.0% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 20.6% | — |
| Price per million tokens | ||
| Input | $0.245 | $0.12 (best) |
| Output | $0.838 | $0.48 (best) |
| Cached input | — | — |
| Blended (3:1) | $0.393 | $0.21 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 16 providers | Median of 2 providers |
| Limits | ||
| Context window | 1,000,000 tokens | 1,048,576 tokens (best) |
| Max output | 16,384 tokens | 65,536 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | Yes |
| PDFs | No | Yes |
| Audio | No | Yes |
| Video | No | Yes |
| Reasoning | No | Yes |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Open | Proprietary |
| API model ID | — | — |
| API providers | 16 (best) | 2 |
| Released | Apr 5, 2025 | Jun 17, 2025 |
| Knowledge cutoff | Aug 2024 | Jan 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Llama 4 Maverick 17B Instruct$4.13
- Gemini 2.5 Flash-Lite Preview 06-17$2.16
Which should you choose?
Which is better: Llama 4 Maverick 17B Instruct or Gemini 2.5 Flash-Lite Preview 06-17?
Gemini 2.5 Flash-Lite Preview 06-17 is the better all-round choice, scoring 67/100 against Llama 4 Maverick 17B Instruct (57). It leads on capability, price and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Llama 4 Maverick 17B Instruct or Gemini 2.5 Flash-Lite Preview 06-17?
Gemini 2.5 Flash-Lite Preview 06-17 is cheaper at $0.12 input / $0.48 output per million tokens (median across 2 API providers). Llama 4 Maverick 17B Instruct costs $0.245 input / $0.838 output per million tokens (median across 16 API providers). At a typical mix of three input tokens to one output token, that is $0.21 per million tokens for Gemini 2.5 Flash-Lite Preview 06-17 versus $0.393 for Llama 4 Maverick 17B Instruct (1.9× as much).
Which scores higher on benchmarks?
Gemini 2.5 Flash-Lite Preview 06-17 scores higher on the Capabilities Index (ECI): Gemini 2.5 Flash-Lite Preview 06-17 133.8 (#132 of 165) and Llama 4 Maverick 17B Instruct 132.3 (#135 of 165). The confidence ranges of the top two overlap (130.1–136.1 vs 128.6–134.2), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Llama 4 Maverick 17B Instruct and Gemini 2.5 Flash-Lite Preview 06-17 yet, so there is no like-for-like coding score. On overall capability, Gemini 2.5 Flash-Lite Preview 06-17 leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.
Which has the bigger context window?
Gemini 2.5 Flash-Lite Preview 06-17 has the largest context window at 1,048,576 tokens, against 1,000,000 for Llama 4 Maverick 17B Instruct. Maximum output per response: Llama 4 Maverick 17B Instruct up to 16,384, Gemini 2.5 Flash-Lite Preview 06-17 up to 65,536 tokens.
Which can read images, PDFs, audio or video?
Llama 4 Maverick 17B Instruct accepts text and images; Gemini 2.5 Flash-Lite Preview 06-17 accepts text, images, PDFs, audio and video. Gemini 2.5 Flash-Lite Preview 06-17 handles the widest range of inputs.
Are any of these open source?
Llama 4 Maverick 17B Instruct publishes its weights and can be self-hosted; Gemini 2.5 Flash-Lite Preview 06-17 is proprietary.
Which is newer?
Gemini 2.5 Flash-Lite Preview 06-17 is the newest, released Jun 17, 2025. Llama 4 Maverick 17B Instruct came out Apr 5, 2025. Knowledge cutoff: Llama 4 Maverick 17B Instruct Aug 2024, Gemini 2.5 Flash-Lite Preview 06-17 Jan 2025.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Nov 2, 2026.
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