Compare/DeepSeek V4 Flash (Non-reasoning) vs Qwen2.5 Turbo

DeepSeek V4 Flash (Non-reasoning)vsQwen2.5 Turbo

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

DeepSeek

DeepSeek V4 Flash (Non-reasoning)

Input
$0.09/M
Output
$0.2/M
Speed
TTFT
Alibaba

Qwen2.5 Turbo

Input
$0.05/M
Output
$0.2/M
Speed
102 tok/s
TTFT
2.17s

Winner by Category

Cheaper
Qwen2.5 Turbo
Faster (tok/s)
Qwen2.5 Turbo
Lower Latency
Qwen2.5 Turbo
Benchmarks (1-0)
DeepSeek V4 Flash (Non-reasoning)

Pricing Comparison

MetricDeepSeek V4 Flash (Non-reasoning)Qwen2.5 Turbo
Input ($/M tokens)$0.09$0.05
Output ($/M tokens)$0.2$0.2
Cost for 1M input + 100K output tokens:
DeepSeek V4 Flash (Non-reasoning)$0.11
Qwen2.5 Turbo$0.07

Speed Comparison

Output Speed (tokens/s) — higher is better
DeepSeek V4 Flash (Non-reasoning)
Qwen2.5 Turbo
102 tok/s
Time to First Token (seconds) — lower is better
DeepSeek V4 Flash (Non-reasoning)
Qwen2.5 Turbo
2.17s

Editorial Analysis

Verdict. DeepSeek V4 Flash (Non-reasoning) wins the overall benchmark matchup 1–0 across 1 overlapping categories, but raw benchmark score is only one input to the decision.

Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, Qwen2.5 Turbo is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Qwen2.5 Turbo makes more sense when output volume is low and absolute reasoning quality justifies the premium.

Strengths. DeepSeek V4 Flash (Non-reasoning) is strongest on Intelligence Index (29.3). Qwen2.5 Turbo leads on Intelligence Index (6.0).

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 V4 Flash (Non-reasoning) costs $5.70 ($68/year); Qwen2.5 Turbo costs $4.50 ($54/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): DeepSeek V4 Flash (Non-reasoning) ≈ $0.85/run, Qwen2.5 Turbo ≈ $0.65/run. At agent/realtime scale (200M input / 100M output per million requests): DeepSeek V4 Flash (Non-reasoning) ≈ $38/run, Qwen2.5 Turbo ≈ $30/run. Qwen2.5 Turbo 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
29.36.0
Coding Index
Math Index
GPQA Diamond
MMLU-Pro
LiveCodeBench
AIME 2025
MATH-500
Humanity's Last Exam
SciCode
IFBench
TerminalBench
DeepSeek V4 Flash (Non-reasoning)1 wins
0 winsQwen2.5 Turbo

Frequently Asked Questions

Which is cheaper, DeepSeek V4 Flash (Non-reasoning) or Qwen2.5 Turbo?

Qwen2.5 Turbo is cheaper overall. Its blended price (3:1 input/output ratio) is $0.09/M tokens vs $0.12/M for DeepSeek V4 Flash (Non-reasoning).

Which model performs better on benchmarks?

DeepSeek V4 Flash (Non-reasoning) wins 1 out of 12 benchmarks compared to 0 for Qwen2.5 Turbo. See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

Qwen2.5 Turbo generates tokens faster at 102 tok/s vs — tok/s. However, Qwen2.5 Turbo has lower time-to-first-token (2.17s vs —s).

When should I use DeepSeek V4 Flash (Non-reasoning) vs Qwen2.5 Turbo?

Choose based on your priorities: Qwen2.5 Turbo for lower cost, DeepSeek V4 Flash (Non-reasoning) for stronger benchmark performance, and Qwen2.5 Turbo for faster generation. For latency-sensitive apps, check the TTFT comparison above.