| Signal | Mercury Coder | Delta | Llama 4 Scout |
|---|---|---|---|
Capabilities | 50 | -17 | |
Benchmarks | 54 | +3 | |
Pricing | 99 | 0 | |
Context window size | 81 | -6 | |
Recency | 71 | +5 | |
Output Capacity | 75 | +5 | |
| Overall Result | 3 wins | of 6 | 3 wins |
Score History
55.3
current score
Mercury Coder
right now
54.2
current score
Inception
Meta
Llama 4 Scout saves you $39.50/month
That's $474.00/year compared to Mercury Coder at your current usage level of 100K calls/month.
| Metric | Mercury Coder | Llama 4 Scout | Winner |
|---|---|---|---|
| Overall Score | 55 | 54 | Mercury Coder |
| Rank | #144 | #146 | Mercury Coder |
| Quality Rank | #144 | #146 | Mercury Coder |
| Adoption Rank | #144 | #146 | Mercury Coder |
| Parameters | -- | -- | -- |
| Context Window | 128K | 328K | Llama 4 Scout |
| Pricing | $0.25/$0.75/M | $0.08/$0.30/M | -- |
| Signal Scores | |||
| Capabilities | 50 | 67 | Llama 4 Scout |
| Benchmarks | 54 | 52 | Mercury Coder |
| Pricing | 99 | 100 | Llama 4 Scout |
| Context window size | 81 | 88 | Llama 4 Scout |
| Recency | 71 | 66 | Mercury Coder |
| Output Capacity | 75 | 70 | Mercury Coder |
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 55/100 (rank #144), placing it in the top 51% of all 290 models tracked.
Scores 54/100 (rank #146), placing it in the top 50% of all 290 models tracked.
With only a 1-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.
Llama 4 Scout offers 62% better value per quality point. At 1M tokens/day, you'd spend $5.70/month with Llama 4 Scout vs $15.00/month with Mercury Coder - a $9.30 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
Higher benchmark score (0/100) indicates stronger performance on coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Faster response time (speed score 0/100) is critical for user-facing chat. Llama 4 Scout also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (328K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.30/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (55/100) 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
Mercury Coder and Llama 4 Scout are extremely close in overall performance (only 1.0999999999999943 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Mercury Coder
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 4 Scout
62% lower pricing; better value at scale
Best for Reliability
Mercury Coder
Higher uptime and faster response speeds
Best for Prototyping
Mercury Coder
Stronger community support and better developer experience
Best for Production
Mercury Coder
Wider enterprise adoption and proven at scale
by Inception
| Capability | Mercury Coder | Llama 4 Scout |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Inception
Meta
Llama 4 Scout saves you $0.8460/month
That's 63% cheaper than Mercury Coder 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 | Mercury Coder | Llama 4 Scout |
|---|---|---|
| Context Window | 128K | 328K |
| Max Output Tokens | 32,000 | 16,384 |
| Open Source | No | Yes |
| Created | Apr 30, 2025 | Apr 5, 2025 |
Mercury Coder scores 55/100 (rank #144) compared to Llama 4 Scout's 54/100 (rank #146), giving it a 1-point advantage. Mercury Coder is the stronger overall choice, though Llama 4 Scout may excel in specific areas like cost efficiency.
Mercury Coder is ranked #144 and Llama 4 Scout is ranked #146 out of 290+ AI models. Rankings use a composite score combining benchmark performance (90%) from MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations, with capabilities and context window as tiebreakers (10%). Scores update hourly.
Llama 4 Scout is cheaper at $0.30/M output tokens vs Mercury Coder's $0.75/M output tokens - 2.5x more expensive. Input token pricing: Mercury Coder at $0.25/M vs Llama 4 Scout at $0.08/M.
Llama 4 Scout has a larger context window of 327,680 tokens compared to Mercury Coder's 128,000 tokens. A larger context window means the model can process longer documents and conversations.