| Signal | Ling-2.6-1T | Delta | Qwen3.6 27B |
|---|---|---|---|
Capabilities | 50 | -33 | |
Pricing | 98 | +1 | |
Context window size | 86 | -- | |
Recency | 100 | -- | |
Output Capacity | 75 | -7 | |
| Overall Result | 1 wins | of 5 | 2 wins |
Score History
40
current score
Tied
right now
40
current score
inclusionai
Alibaba
Ling-2.6-1T saves you $37.00/month
That's $444.00/year compared to Qwen3.6 27B at your current usage level of 100K calls/month.
| Metric | Ling-2.6-1T | Qwen3.6 27B | Winner |
|---|---|---|---|
| Overall Score | 40 | 40 | -- |
| Rank | #202 | #201 | Qwen3.6 27B |
| Quality Rank | #202 | #201 | Qwen3.6 27B |
| Adoption Rank | #202 | #201 | Qwen3.6 27B |
| Parameters | -- | 27B | -- |
| Context Window | 262K | 262K | -- |
| Pricing | $0.30/$2.50/M | $0.32/$3.20/M | -- |
| Signal Scores | |||
| Capabilities | 50 | 83 | Qwen3.6 27B |
| Pricing | 98 | 97 | Ling-2.6-1T |
| Context window size | 86 | 86 | Ling-2.6-1T |
| Recency | 100 | 100 | Ling-2.6-1T |
| Output Capacity | 75 | 82 | Qwen3.6 27B |
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 40/100 (rank #202), placing it in the top 31% of all 290 models tracked.
Scores 40/100 (rank #201), placing it in the top 31% 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.
Ling-2.6-1T offers 20% better value per quality point. At 1M tokens/day, you'd spend $42.00/month with Ling-2.6-1T vs $52.80/month with Qwen3.6 27B - a $10.80 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. Ling-2.6-1T also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (262K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($2.50/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (40/100) 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
Ling-2.6-1T and Qwen3.6 27B 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
Ling-2.6-1T
Marginally better benchmark scores; both are excellent
Best for Cost
Ling-2.6-1T
20% lower pricing; better value at scale
Best for Reliability
Ling-2.6-1T
Higher uptime and faster response speeds
Best for Prototyping
Ling-2.6-1T
Stronger community support and better developer experience
Best for Production
Ling-2.6-1T
Wider enterprise adoption and proven at scale
by inclusionai
| Capability | Ling-2.6-1T | Qwen3.6 27B |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
inclusionai
Alibaba
Ling-2.6-1T saves you $0.8760/month
That's 20% cheaper than Qwen3.6 27B 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 | Ling-2.6-1T | Qwen3.6 27B |
|---|---|---|
| Context Window | 262K | 262K |
| Max Output Tokens | 32,768 | 81,920 |
| Open Source | No | Yes |
| Created | Apr 23, 2026 | Apr 27, 2026 |