Llama 4 Maverick vs Nova 2 Lite
| Signal | Llama 4 Maverick | Delta | Nova 2 Lite |
|---|---|---|---|
Capabilities | 67 | -- | |
Benchmarks | 70 | +12 | |
Pricing | 99 | +2 | |
Context window size | 96 | +0 | |
Recency | 44 | -44 | |
Output Capacity | 70 | -10 | |
| Overall Result | 3 wins | of 6 | 2 wins |
Score History
67.9
current score
Llama 4 Maverick
right now
60.5
current score
Llama 4 Maverick
Meta
Nova 2 Lite
Amazon
Llama 4 Maverick saves you $95.00/month
That's $1140.00/year compared to Nova 2 Lite at your current usage level of 100K calls/month.
| Metric | Llama 4 Maverick | Nova 2 Lite | Winner |
|---|---|---|---|
| Overall Score | 68 | 61 | Llama 4 Maverick |
| Rank | #173 | #207 | Llama 4 Maverick |
| Quality Rank | #173 | #207 | Llama 4 Maverick |
| Adoption Rank | #173 | #207 | Llama 4 Maverick |
| Parameters | -- | -- | -- |
| Context Window | 1049K | 1000K | Llama 4 Maverick |
| Pricing | $0.20/$0.80/M | $0.30/$2.50/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 67 | Llama 4 Maverick |
| Benchmarks | 70 | 58 | Llama 4 Maverick |
| Pricing | 99 | 98 | Llama 4 Maverick |
| Context window size | 96 | 95 | Llama 4 Maverick |
| Recency | 44 | 88 | Nova 2 Lite |
| Output Capacity | 70 | 80 | Nova 2 Lite |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.
Scores 68/100 (rank #173), placing it in the top 41% of all 290 models tracked.
Scores 61/100 (rank #207), placing it in the top 29% of all 290 models tracked.
Llama 4 Maverick has a 7-point advantage, which typically translates to noticeably better performance on complex reasoning, code generation, and multi-step tasks.
Choose Llama 4 Maverick when you need:
- High-volume production workloads where API costs must be minimized
- Self-hosted deployments where you need full control over the model
Choose Nova 2 Lite when you need:
- Step-by-step reasoning and chain-of-thought problem solving
Llama 4 Maverick offers 64% better value per quality point. At 1M tokens/day, you'd spend $15.00/month with Llama 4 Maverick vs $42.00/month with Nova 2 Lite - a $27.00 monthly difference.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Suitable for user-facing chat with competitive response times. Llama 4 Maverick also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (1049K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.80/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (68/100) correlates with better nuance, coherence, and style in long-form content
Image understanding & OCR
Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly
Llama 4 Maverick has a moderate advantage with a 7.400000000000006-point lead in composite score. It wins on more signal dimensions, but Nova 2 Lite has specific strengths that could make it the better choice for certain workflows.
By Use Case
Best for Quality
Llama 4 Maverick
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 4 Maverick
64% lower pricing; better value at scale
Best for Reliability
Llama 4 Maverick
Higher uptime and faster response speeds
Best for Prototyping
Llama 4 Maverick
Stronger community support and better developer experience
Best for Production
Llama 4 Maverick
Wider enterprise adoption and proven at scale
by Meta
- Choose for Quality - Marginally better benchmark scores; both are excellent
- Choose for Cost - 64% lower pricing; better value at scale
- Choose for Reliability - Higher uptime and faster response speeds
- Choose for Prototyping - Stronger community support and better developer experience
- Choose for Production - Wider enterprise adoption and proven at scale
| Capability | Llama 4 Maverick | Nova 2 Lite |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Llama 4 Maverick
Meta
Nova 2 Lite
Amazon
Llama 4 Maverick saves you $2.22/month
That's 63% cheaper than Nova 2 Lite at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | Llama 4 Maverick | Nova 2 Lite |
|---|---|---|
| Context Window | 1.0M | 1M |
| Max Output Tokens | 16,384 | 65,535 |
| Open Source | Yes | No |
| Created | Apr 5, 2025 | Dec 2, 2025 |
Nova 2 Lite's higher ranking likely reflects its broader modality support (video and file processing) and Reasoning capability, which matter more for complex coding workflows than raw performance scores indicate. The $2.5/M output pricing (vs $0.6/M) positions it as an enterprise tool where the 66K max output tokens (4.1x Llama's 16K) justifies the premium for long-form code generation tasks.
Llama 4 Maverick would save approximately $95,000/month on output costs alone ($0.6/M vs $2.5/M), while still offering JSON Mode for structured API responses that Nova 2 Lite lacks. The 16K output limit is sufficient for most code review comments, making Nova's 66K output advantage irrelevant for this use case.
Nova's Reasoning capability becomes crucial for multi-file debugging where it can process video walkthroughs and analyze complex dependency chains, justifying its 2x higher input cost ($0.3/M vs $0.15/M). However, Llama 4 Maverick's open-source nature allows fine-tuning on proprietary codebases, potentially closing the reasoning gap for domain-specific debugging at 1/4 the output cost.
Llama 4 Maverick's JSON Mode support suggests optimized structured output generation that compensates for lacking Reasoning capabilities, achieving the same 54/100 score at $0.15/M input (half Nova's price). Meta's open-source approach likely leverages community contributions for efficiency gains, while Amazon's closed model invests in broader modality support (video/file processing) that adds overhead without improving core coding benchmarks.
Migration would require rebuilding video-based workflows since Llama only supports text and image inputs, potentially negating the 76% output cost savings ($0.6/M vs $2.5/M). Teams would need to extract 4 separate 16K chunks to match Nova's single 66K output capacity, adding complexity that may offset Llama's $1.9/M output price advantage for documentation-heavy projects.