DeepSeek-V3 vs Qwen2.5-Max
DeepSeek vs Qwen · Prices checked November 11, 2026
On blended cost DeepSeek-V3 undercuts Qwen2.5-Max by about 6.2×: $0.4910 per million tokens versus $3.04. A single 100K-input, 10K-output job costs $0.0409 on DeepSeek-V3 against $0.2240 on Qwen2.5-Max.
Pricing and capacity
RatesUSD per million tokens. Prices checked November 11, 2026.
| Metric | DeepSeek-V3 | Qwen2.5-Max | Difference |
|---|---|---|---|
| Input / 1M tokens | $0.3200 | $1.60 | 5.0× |
| Output / 1M tokens | $0.8900 | $6.40 | 7.2× |
| Blended · 70/30calculated | $0.4910 | $3.04 | 6.2× |
| Blended · 50/50calculated | $0.6050 | $4.00 | 6.6× |
| Blended · 80/20calculated | $0.4340 | $2.56 | 5.9× |
| Blended · 20/80calculated | $0.7760 | $5.44 | 7.0× |
| Job cost · 100K + 10Kcalculated | $0.0409 | $0.2240 | 5.5× |
| Job cost · 1M + 100Kcalculated | $0.4090 | $2.24 | 5.5× |
| Context window | 64,000 | 32,000 | 2.0× |
Input and output rates are as published by each provider. Blended and job-cost rows are calculated comparison metrics, not published prices — how they are derived.
Of the 4 shared benchmarks, Qwen2.5-Max takes 3. The widest gap is on LMArena Elo, where Qwen2.5-Max leads by 16 points. On context, DeepSeek-V3 is the larger at 64,000 tokens versus 32,000.
Benchmarks
| Benchmark | DeepSeek-V3 | Qwen2.5-Max | Leader |
|---|---|---|---|
| MMLU | 88.5% | 87% | DeepSeek-V3 |
| GPQA Diamond | 59.1% | 60% | Qwen2.5-Max |
| HumanEval | 89% | 90% | Qwen2.5-Max |
| LMArena Elo | 1358 Elo | 1374 Elo | Qwen2.5-Max |
Only suites both models report are listed, so a missing score is never shown as a loss. Figures are provider-published or from public leaderboards and are not independently re-run.
What each model is for
DeepSeek-V3
DeepSeek · Frontier
Open-weights frontier model that matched GPT-4-class quality at a fraction of the inference cost. Sparked the late-2024 cost reset.
- Frontier quality, open weights
- Aggressive pricing
- Strong code & math
- MoE architecture
Qwen2.5-Max
Qwen · Flagship
Alibaba's closed-weights flagship. Frontier-quality MoE that competes with Claude / GPT on Chinese benchmarks at competitive pricing.
- Frontier on Chinese benchmarks
- MoE inference economics
- Strong tool use
- Multilingual
Frequently asked questions
Which is cheaper, DeepSeek-V3 or Qwen2.5-Max?
On blended cost DeepSeek-V3 undercuts Qwen2.5-Max by about 6.2×: $0.4910 per million tokens versus $3.04. A single 100K-input, 10K-output job costs $0.0409 on DeepSeek-V3 against $0.2240 on Qwen2.5-Max. Blended cost weights input at 70% and output at 30%; price your own token split for an exact answer, since the cheaper model on a blended basis is not always cheaper for your workload.
Which performs better, DeepSeek-V3 or Qwen2.5-Max?
Of the 4 shared benchmarks, Qwen2.5-Max takes 3. The widest gap is on LMArena Elo, where Qwen2.5-Max leads by 16 points. These are the figures each provider or public leaderboard publishes, not results we re-run, and a benchmark average can hide the specific behaviour your workload depends on.
Which has the larger context window, DeepSeek-V3 or Qwen2.5-Max?
On context, DeepSeek-V3 is the larger at 64,000 tokens versus 32,000. The context window caps everything in a single request: prompt, retrieved documents, conversation history and the model’s own output all share it.
Can I compare DeepSeek-V3 and Qwen2.5-Max on my own numbers?
Yes. Open both in the side-by-side comparison tool to see every field at once, or use the token calculator to price your actual prompt and monthly request volume against each model. Both are free and need no sign-up.
Price it for your own workload
Related matchups
Terms used on this page
Prices and scores are recomputed on every deploy from the catalogue. Prices checked November 11, 2026.