Pairwise benchmark snapshot · Aug 1, 2026
In this head-to-head showdown, Yi-Large delivers higher overall intelligence and human-preferred responses (Elo 1,430 vs 1,358), while DeepSeek R1 leads in SWE-bench software engineering benchmarks (65.2%), while Yi-Large is more budget-friendly at $0.3/1M per 1M output tokens. Review the complete breakdown below to determine which model best fits your performance and budget requirements.
9 direct benchmark disciplines evaluated across capability, speed, and cost
Open-weights reasoning model trained with large-scale RL.
01.AI full-scale dense model for complex instruction following.
Simulate accuracy gains vs added response time & token cost
Reasoning models “think before answering” by generating internal reasoning tokens. Higher effort improves math, coding, and logic accuracy, but increases response delay and token costs.
Δ 72 Arena Elo pts
90 tok/s
$0.3 / 1M output
Relative percentile scores computed across all active models in the benchmark catalog.
Comparing top Western standard models with China's leading frontier rival across 6 skill dimensions. Tap any spoke or dot to inspect.
Category wins across reasoning intelligence, generation speed, and token cost.
Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy
Generation throughput and time to first token responsiveness
Cost per million tokens and max context window length
| Benchmark | DeepSeek R1 | Yi-Large | Advantage Delta |
|---|---|---|---|
| Preference Elo | +72 | ||
| Coding Elo | — | — | |
| LiveBench | +1.5% | ||
| SWE-bench | +11.4% | ||
| GPQA Diamond | +7.4% | ||
| Time to first token | +200 ms | ||
| Output speed | 62 tok/s | 90 tok/s | +28 tok/s |
| Input price | $0.55/1M | $0.3/1M | +$0.25/1M |
| Output price | $2.19/1M | $0.3/1M | +$1.89/1M |
| Context window | +95k |
Simulate monthly production API costs in USD (US Dollar).
Yi-Large is estimated to save $37.10/month ($445/year).
Higher SWE-bench (65.2%).
Lower output list price ($0.3/1M).
Lower TTFT (220 ms).
Larger window (128k).
Yi-Large is the side marked multimodal in the catalog.
| Target Workload | Recommended Pick | Evaluation Rationale |
|---|---|---|
| Repo / coding agents | DeepSeek R1 | Higher SWE-bench (65.2%). |
| High-volume chat | Yi-Large | Lower output list price ($0.3/1M). |
| Voice / low-latency UI | Yi-Large | Lower TTFT (220 ms). |
| Long-document RAG | DeepSeek R1 | Larger window (128k). |
| Screenshots / vision | Yi-Large | Yi-Large is the side marked multimodal in the catalog. |
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Verified Head-to-Head Telemetry
