| Signal | Coder Large | Delta | Qwen2.5 7B Instruct |
|---|---|---|---|
Capabilities | 17 | -33 | |
Pricing | 99 | -1 | |
Context window size | 65 | -9 | |
Recency | 57 | +37 | |
Output Capacity | 20 | -55 | |
Benchmarks | 0 | -39 | |
| Overall Result | 1 wins | of 6 | 5 wins |
Score History
39.2
current score
Coder Large
right now
38.2
current score
arcee-ai
Alibaba
Qwen2.5 7B Instruct saves you $81.00/month
That's $972.00/year compared to Coder Large at your current usage level of 100K calls/month.
| Metric | Coder Large | Qwen2.5 7B Instruct | Winner |
|---|---|---|---|
| Overall Score | 39 | 38 | Coder Large |
| Rank | #299 | #301 | Coder Large |
| Quality Rank | #299 | #301 | Coder Large |
| Adoption Rank | #299 | #301 | Coder Large |
| Parameters | -- | 7B | -- |
| Context Window | 33K | 131K | Qwen2.5 7B Instruct |
| Pricing | $0.50/$0.80/M | $0.04/$0.10/M | -- |
| Signal Scores | |||
| Capabilities | 17 | 50 | Qwen2.5 7B Instruct |
| Pricing | 99 | 100 | Qwen2.5 7B Instruct |
| Context window size | 65 | 73 | Qwen2.5 7B Instruct |
| Recency | 57 | 20 | Coder Large |
| Output Capacity | 20 | 75 | Qwen2.5 7B Instruct |
| Benchmarks | -- | 39 | Qwen2.5 7B Instruct |
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 39/100 (rank #299), placing it in the top -3% of all 290 models tracked.
Scores 38/100 (rank #301), placing it in the top -3% 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.
Qwen2.5 7B Instruct offers 89% better value per quality point. At 1M tokens/day, you'd spend $2.10/month with Qwen2.5 7B Instruct vs $19.50/month with Coder Large - a $17.40 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. Qwen2.5 7B Instruct also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (131K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.10/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (39/100) correlates with better nuance, coherence, and style in long-form content
Coder Large and Qwen2.5 7B Instruct are extremely close in overall performance (only 1 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Coder Large
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen2.5 7B Instruct
89% lower pricing; better value at scale
Best for Reliability
Coder Large
Higher uptime and faster response speeds
Best for Prototyping
Coder Large
Stronger community support and better developer experience
Best for Production
Coder Large
Wider enterprise adoption and proven at scale
by arcee-ai
| Capability | Coder Large | Qwen2.5 7B Instruct |
|---|---|---|
| Vision (Image Input) | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
arcee-ai
Alibaba
Qwen2.5 7B Instruct saves you $1.67/month
That's 90% cheaper than Coder Large 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 | Coder Large | Qwen2.5 7B Instruct |
|---|---|---|
| Context Window | 33K | 131K |
| Max Output Tokens | -- | 32,768 |
| Open Source | No | Yes |
| Created | May 5, 2025 | Oct 16, 2024 |