| Signal | Mercury | Delta | Mercury Coder |
|---|---|---|---|
Capabilities | 50 | -- | |
Benchmarks | 54 | -- | |
Pricing | 99 | -- | |
Context window size | 81 | -- | |
Recency | 81 | +10 | |
Output Capacity | 75 | -- | |
| Overall Result | 1 wins | of 6 | 0 wins |
Score History
55.3
current score
Tied
right now
55.3
current score
Inception
Inception
| Metric | Mercury | Mercury Coder | Winner |
|---|---|---|---|
| Overall Score | 55 | 55 | -- |
| Rank | #143 | #144 | Mercury |
| Quality Rank | #143 | #144 | Mercury |
| Adoption Rank | #143 | #144 | Mercury |
| Parameters | -- | -- | -- |
| Context Window | 128K | 128K | -- |
| Pricing | $0.25/$0.75/M | $0.25/$0.75/M | -- |
| Signal Scores | |||
| Capabilities | 50 | 50 | Mercury |
| Benchmarks | 54 | 54 | Mercury |
| Pricing | 99 | 99 | Mercury |
| Context window size | 81 | 81 | Mercury |
| Recency | 81 | 71 | Mercury |
| Output Capacity | 75 | 75 | Mercury |
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 #143), placing it in the top 51% of all 290 models tracked.
Scores 55/100 (rank #144), placing it in the top 51% of all 290 models tracked.
With only a 0-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.
Both models are priced similarly, so the decision comes down to quality and features rather than cost.
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. Mercury also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (128K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.75/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
Mercury and Mercury Coder are extremely close in overall performance (only 0 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Mercury
Marginally better benchmark scores; both are excellent
Best for Cost
Mercury
0% lower pricing; better value at scale
Best for Reliability
Mercury
Higher uptime and faster response speeds
Best for Prototyping
Mercury
Stronger community support and better developer experience
Best for Production
Mercury
Wider enterprise adoption and proven at scale
by Inception
| Capability | Mercury | Mercury Coder |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Inception
Inception
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | Mercury | Mercury Coder |
|---|---|---|
| Context Window | 128K | 128K |
| Max Output Tokens | 32,000 | 32,000 |
| Open Source | No | No |
| Created | Jun 26, 2025 | Apr 30, 2025 |
Both Mercury and Mercury Coder score 55/100, making them extremely close competitors. Choose based on pricing, provider ecosystem, or specific capability requirements.
Mercury is ranked #143 and Mercury Coder is ranked #144 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.
Mercury is cheaper at $0.75/M output tokens vs Mercury Coder's $0.75/M output tokens - 1.0x more expensive. Input token pricing: Mercury at $0.25/M vs Mercury Coder at $0.25/M.
Mercury has a larger context window of 128,000 tokens compared to Mercury Coder's 128,000 tokens. A larger context window means the model can process longer documents and conversations.