AI model comparison
Qwen3.8 27B vs DeepSeek V4 Pro 0423
Pricing, context window, benchmarks, and features compared side by side. Or skip the guesswork and send one prompt to both.
Qwen3.8 27B is an open-weight dense vision-language model from Qwen.
- Input / 1M
- $0.04
- Output / 1M
- $1.35
- Context
- 1M
DeepSeek V4 Pro is a large-scale Mixture-of-Experts model from DeepSeek with 1.6T total parameters and 49B activated parameters, supporting a 1M-token context window.
- Input / 1M
- $0.43
- Output / 1M
- $0.87
- Context
- 1.05M
Comparing image generators? Compare image models
At a glance
Quick verdict
How the two models stack up on the things people ask about most.
Lower price
Qwen3.8 27B
1.7x cheaper for a typical chat
Higher intelligence score
Qwen3.8 27B
33.7 vs 30.4 on Artificial Analysis
Larger context window
DeepSeek V4 Pro 0423
1.05M vs 1M tokens
Newer release
Qwen3.8 27B
Released August 14, 2026
Benchmarks
Benchmark scores
Independent scores from Artificial Analysis. Higher is better.
Intelligence index
Qwen3.8 27B33.7DeepSeek V4 Pro 042330.4Coding index
Qwen3.8 27B68.1DeepSeek V4 Pro 042359.4Agentic index
Qwen3.8 27B45.8DeepSeek V4 Pro 042326.3Pricing
Qwen3.8 27B vs DeepSeek V4 Pro 0423 API pricing
Per-token API rates. Cheaper option highlighted.
| Metric | Qwen3.8 27B | DeepSeek V4 Pro 0423 |
|---|---|---|
| Input tokensPer 1M tokens | $0.04 | $0.43 |
| Output tokensPer 1M tokens | $1.35 | $0.87 |
| Cached input (read)Per 1M tokens | $0.02 | $0.04 |
| Cache writePer 1M tokens | Not available | Not available |
What it costs in practice
Estimated cost per 1,000 requests at standard rates. Coding and long-document figures also show the cost when the input is already cached.
Chat message
2K in, 500 out
Qwen3.8 27B$0.76DeepSeek V4 Pro 0423$1.30Coding task
30K in, 4K out
Qwen3.8 27B$6.60$5.94 cachedDeepSeek V4 Pro 0423$16.50$4.56 cachedLong document summary
150K in, 2K out
Qwen3.8 27B$8.70$5.40 cachedDeepSeek V4 Pro 0423$66.88$7.17 cachedSpecs
Context window and capabilities
How much each model can read, how much it can write, and what it accepts as input.
| Metric | Qwen3.8 27B | DeepSeek V4 Pro 0423 |
|---|---|---|
| Context window | 1M tokens | 1.05M tokens |
| Max output | 66K tokens | 384K tokens |
| Input types | Image, Text, Video | Text |
| Output types | Text | Text |
| Reasoning effort levels | Extra high, Medium, Low, None | Extra high, High, None |
| Default reasoning effort | Extra high | High |
| Release date | August 14, 2026 | April 24, 2026 |
Features
Supported features
API features available for each model.
| Metric | Qwen3.8 27B | DeepSeek V4 Pro 0423 |
|---|---|---|
| Tool calling | Supported | Supported |
| Structured outputs | Supported | Supported |
| JSON mode | Supported | Supported |
| Reasoning | Supported | Supported |
| Temperature | Supported | Supported |
| Stop sequences | Supported | Supported |
| Deterministic seed | Supported | Supported |
| Verbosity control | Not supported | Not supported |
Keep exploring
More on these models
Other head-to-heads, the full model page, and the alternatives list for each model.
Qwen3.8 27BView full model details- Qwen3.8 27B vs MiMo-V2.6-Flash
- Qwen3.8 27B vs DeepSeek V4.1 Flash 1017
- Qwen3.8 27B vs GPT-6 Luna
- Qwen3.8 27B vs DeepSeek V4 Flash 0731
- Qwen3.8 27B vs GPT-5.6 Luna
- Qwen3.8 27B vs MiniMax M3
- Qwen3.8 27B vs GLM 5.2
- Qwen3.8 27B vs DeepSeek V4 Pro 0813
- Qwen3.8 27B vs DeepSeek V4 Flash Vision Exp
- Qwen3.8 27B vs Gemini 3.6 Flash
- Qwen3.8 27B vs Muse Spark 1.1
- Qwen3.8 27B vs Qwen3.7 Max
DeepSeek V4 Pro 0423View full model details- DeepSeek V4 Pro 0423 vs DeepSeek V4.1 Flash
- DeepSeek V4 Pro 0423 vs DeepSeek V4.1 Flash 1017
- DeepSeek V4 Pro 0423 vs DeepSeek V4 Flash 0731
- DeepSeek V4 Pro 0423 vs Hy3
- DeepSeek V4 Pro 0423 vs DeepSeek V4 Flash 0423
- DeepSeek V4 Pro 0423 vs MiniMax M3
- DeepSeek V4 Pro 0423 vs DeepSeek V4 Pro 0813
- DeepSeek V4 Pro 0423 vs Solar Pro 4
- DeepSeek V4 Pro 0423 vs DeepSeek V4 Flash Vision Exp
- DeepSeek V4 Pro 0423 vs MiMo-V2.5-Pro
- DeepSeek V4 Pro 0423 vs Nemotron 3.5 Super VL
Our take
Qwen3.8 27B is 1.7x cheaper for a typical chat. Qwen3.8 27B scores higher on the Artificial Analysis Intelligence Index (33.7 vs 30.4). DeepSeek V4 Pro 0423 has the larger context window (1.05M vs 1M tokens). The right pick depends on your workload, so the quickest way to settle it is to send the same prompt to both and compare.
FAQ
Frequently asked questions
Is Qwen3.8 27B or DeepSeek V4 Pro 0423 cheaper?
Qwen3.8 27B is cheaper for a typical chat (2K input tokens and 500 output tokens). Qwen3.8 27B costs $0.04 per 1M input tokens and $1.35 per 1M output tokens, while DeepSeek V4 Pro 0423 costs $0.43 per 1M input tokens and $0.87 per 1M output tokens.
Which has a bigger context window, Qwen3.8 27B or DeepSeek V4 Pro 0423?
DeepSeek V4 Pro 0423 supports up to 1.05M tokens of context, compared with 1M for Qwen3.8 27B.
Is Qwen3.8 27B smarter than DeepSeek V4 Pro 0423?
On the Artificial Analysis Intelligence Index, Qwen3.8 27B scores 33.7 and DeepSeek V4 Pro 0423 scores 30.4, putting Qwen3.8 27B ahead. Benchmarks don't capture everything, so the best test is running your own prompts through both.
Can I use Qwen3.8 27B and DeepSeek V4 Pro 0423 at the same time?
Yes. Shortcut Chat sends one prompt to multiple models at once, so you can see Qwen3.8 27B and DeepSeek V4 Pro 0423 answer side by side and keep the better response.
Why choose? Ask both.
Send one prompt to Qwen3.8 27B and DeepSeek V4 Pro 0423 at the same time. Compare the answers side by side and keep the best one.