💰 Unit Cost Winner DeepSeek V3 $0.14/1M in · $0.28/1M out
⚡ Latency & Speed Winner Qwen 3.8 Flash Next 280ms TTFT · 185 tok/s
💻 Coding (SWE) Winner Qwen 3.8 Flash Next 81% SWE-bench · 95.1% HumanEval
🧠 STEM Reasoning Winner Qwen 3.8 Flash Next 91.7% GPQA · 95.7% MATH-500

Which model is better: DeepSeek V3 or Qwen 3.8 Flash Next?

Qwen 3.8 Flash Next delivers superior capability and speed, outclassing DeepSeek V3 despite higher pricing. Qwen dominates SWE-bench Verified (81.0% vs. 42.0%) and GPQA Diamond (91.7% vs. 59.1%), alongside a 280ms TTFT (130ms faster) and 185 vs. 78 tok/s throughput. DeepSeek V3 is 12.5% cheaper on input ($0.14 vs. $0.16) and $0.28 vs. $0.47 for output.
Verified daily via automated API latency tests and official documentation.

Verified Performance & Unit Economics Delta (12 Dimensions)

Daily synchronized metrics across token pricing, prompt cache economics, latency, throughput, and 5 capability benchmarks.

Metric / Dimension DeepSeek V3 Qwen 3.8 Flash Next Net Delta / Advantage
Input Cost / 1M Tokens $0.14 $0.16 DeepSeek V3 is 1.1x cheaper
Output Cost / 1M Tokens $0.28 $0.47 DeepSeek V3 (40.4% lower)
Prompt Cache Read / 1M $0.014 $0.016 DeepSeek V3 is 1.1x cheaper
Prompt Cache Write / 1M $0.14 $0.20 DeepSeek V3 is 1.4x cheaper
Context Window (Max Output) 131k (8k out) 268k (66k out) Qwen 3.8 Flash Next (2.0x larger context)
Avg TTFT Response Latency 410ms 280ms Qwen 3.8 Flash Next (130ms faster)
Generation Speed (Throughput) 78 tok/s 185 tok/s Qwen 3.8 Flash Next (+107 tok/s faster)
SWE-bench Verified (Coding) 42% 81% Qwen 3.8 Flash Next (+39.0% lead)
GPQA Diamond (PhD STEM) 59.1% 91.7% Qwen 3.8 Flash Next (+32.6% lead)
MMLU-Pro (Multi-Discipline) 75.9% 88.4% Qwen 3.8 Flash Next (+12.5% lead)
MATH-500 (Quantitative Logic) 90.2% 95.7% Qwen 3.8 Flash Next (+5.5% lead)
Inference Value Score (IVR) 75 / 100 85.1 / 100 Qwen 3.8 Flash Next (+10.1 pts)

Interactive Monthly Token Economics & ROI Forecaster

Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.

25.0M Tokens
500K 50M 250M 500M+
5.0M Tokens
100K 25M 75M 150M+
50% Cached
0% (Uncached) 25% 50% (Typical Agent) 95% Max

Estimated Monthly Spend

DeepSeek V3 $3.33

$0.14/1M in · $0.28/1M out

Prompt Cache: $0.014/1M read · $0.14/1M write

Qwen 3.8 Flash Next $4.55

$0.16/1M in · $0.47/1M out

Prompt Cache: $0.016/1M read · $0.20/1M write

Projected Monthly Cost Reduction
$1.22 / mo
(26.9% lower cost with DeepSeek V3)
Open Full Matchup Analysis →
A

When to Choose DeepSeek V3

Choose DeepSeek V3 (DeepSeek) for cost-sensitive batch text processing, drafting, and open-weight self-hosted inference — offering a 131k context window, 410ms TTFT, and $0.14/1M input pricing ($0.014/1M cached reads).

B

When to Choose Qwen 3.8 Flash Next

Choose Qwen 3.8 Flash Next (Alibaba Cloud) for next-generation open-weight multimodal MoE activating only 6B parameters per token for ultra-low-cost enterprise office automation, tool calling, and multilingual repository engineering — offering a 268k context window, 280ms TTFT, and $0.16/1M input pricing ($0.016/1M cached reads).

Related Questions & Decision Factors

Which is cheaper: DeepSeek V3 or Qwen 3.8 Flash Next?

DeepSeek V3 is cheaper at $0.14/1M input tokens ($0.28/1M output) compared to $0.16/1M input ($0.47/1M output) for Qwen 3.8 Flash Next.

Which model has lower TTFT latency and higher tok/s throughput: DeepSeek V3 or Qwen 3.8 Flash Next?

Qwen 3.8 Flash Next delivers faster first-token latency at 280ms TTFT (185 tok/s throughput) compared to 410ms TTFT (78 tok/s) for DeepSeek V3.

How do DeepSeek V3 and Qwen 3.8 Flash Next compare on SWE-bench coding and GPQA Diamond reasoning?

On SWE-bench Verified software engineering, DeepSeek V3 scores 42% versus 81% for Qwen 3.8 Flash Next. On GPQA Diamond graduate-level STEM reasoning, DeepSeek V3 scores 59.1% versus 91.7% for Qwen 3.8 Flash Next (and 90.2% vs 95.7% on MATH-500).

When should you choose DeepSeek V3?

Choose DeepSeek V3 (DeepSeek) for cost-sensitive batch text processing, drafting, and open-weight self-hosted inference — offering a 131k context window, 410ms TTFT, and $0.14/1M input pricing ($0.014/1M cached reads).

When should you choose Qwen 3.8 Flash Next?

Choose Qwen 3.8 Flash Next (Alibaba Cloud) for next-generation open-weight multimodal MoE activating only 6B parameters per token for ultra-low-cost enterprise office automation, tool calling, and multilingual repository engineering — offering a 268k context window, 280ms TTFT, and $0.16/1M input pricing ($0.016/1M cached reads).

Explore High-Priority Related Model Matchups