Compare/DeepSeek R1 0528 (May '25) vs Qwen3.8 27B (low)

DeepSeek R1 0528 (May '25)vsQwen3.8 27B (low)

Side-by-side comparison of pricing, 12 benchmarks, and generation speed.

DeepSeek

DeepSeek R1 0528 (May '25)

Input
$1.35/M
Output
$3/M
Speed
TTFT
Alibaba

Qwen3.8 27B (low)

Input
$0.5/M
Output
$3/M
Speed
65 tok/s
TTFT
2.99s

Winner by Category

Cheaper
Qwen3.8 27B (low)
Faster (tok/s)
Qwen3.8 27B (low)
Lower Latency
Qwen3.8 27B (low)
Benchmarks (0-2)
Qwen3.8 27B (low)

Pricing Comparison

MetricDeepSeek R1 0528 (May '25)Qwen3.8 27B (low)
Input ($/M tokens)$1.35$0.5
Output ($/M tokens)$3$3
Cost for 1M input + 100K output tokens:
DeepSeek R1 0528 (May '25)$1.65
Qwen3.8 27B (low)$0.80

Speed Comparison

Output Speed (tokens/s) — higher is better
DeepSeek R1 0528 (May '25)
Qwen3.8 27B (low)
65 tok/s
Time to First Token (seconds) — lower is better
DeepSeek R1 0528 (May '25)
Qwen3.8 27B (low)
2.99s

Editorial Analysis

Verdict. Qwen3.8 27B (low) takes the aggregate benchmark matchup 2–0 across 2 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.

Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, Qwen3.8 27B (low) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Qwen3.8 27B (low) makes more sense when output volume is low and absolute reasoning quality justifies the premium.

Strengths. DeepSeek R1 0528 (May '25) is strongest on Intelligence Index (20.4). Qwen3.8 27B (low) leads on Coding Index (58.2), Intelligence Index (42.9).

Speed. Speed data is incomplete for this pair; benchmark and price should decide.

Provider. DeepSeek and Alibaba sell to overlapping but distinct developer audiences: DeepSeek tends to ship frontier reasoning models with premium positioning, while Alibaba often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.

Workload cost. Workload scenarios (per million requests at 30M input + 15M output tokens): DeepSeek R1 0528 (May '25) costs $85.50 ($1026/year); Qwen3.8 27B (low) costs $60.00 ($720/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): DeepSeek R1 0528 (May '25) ≈ $12.75/run, Qwen3.8 27B (low) ≈ $8.50/run. At agent/realtime scale (200M input / 100M output per million requests): DeepSeek R1 0528 (May '25) ≈ $570/run, Qwen3.8 27B (low) ≈ $400/run. Qwen3.8 27B (low) becomes more attractive at higher volume — the absolute per-token pricing difference compounds when you ship at scale.

Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
20.442.9
Coding Index
58.2
Math Index
GPQA Diamond
MMLU-Pro
LiveCodeBench
AIME 2025
MATH-500
Humanity's Last Exam
SciCode
IFBench
TerminalBench
DeepSeek R1 0528 (May '25)0 wins
2 winsQwen3.8 27B (low)

Frequently Asked Questions

Which is cheaper, DeepSeek R1 0528 (May '25) or Qwen3.8 27B (low)?

Qwen3.8 27B (low) is cheaper overall. Its blended price (3:1 input/output ratio) is $1.13/M tokens vs $1.76/M for DeepSeek R1 0528 (May '25).

Which model performs better on benchmarks?

Qwen3.8 27B (low) wins 2 out of 12 benchmarks compared to 0 for DeepSeek R1 0528 (May '25). See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

Qwen3.8 27B (low) generates tokens faster at 65 tok/s vs — tok/s. However, Qwen3.8 27B (low) has lower time-to-first-token (2.99s vs —s).

When should I use DeepSeek R1 0528 (May '25) vs Qwen3.8 27B (low)?

Choose based on your priorities: Qwen3.8 27B (low) for lower cost, Qwen3.8 27B (low) for stronger benchmark performance, and Qwen3.8 27B (low) for faster generation. For latency-sensitive apps, check the TTFT comparison above.