Models, measured side by side
DeepSeek V4.1 Flash vs GPT-5.2 Pro
Compare DeepSeek V4.1 Flash, GPT-5.2 Pro by token pricing, context length, benchmark results, speed and tool support.
Monthly workload
Monthly cost breakdown
InputOutput
DeepSeek V4.1 Flash
$7.00 / mo
GPT-5.2 Pro
$1,260 / mo
Performance comparison
DeepSeek V4.1 Flash
39.5
GPT-5.2 Pro
Not listed
Overview
Pricing & capacity
| Pricing & capacity | DeepSeek V4.1 Flash | GPT-5.2 Pro |
|---|---|---|
| Input / 1M tokens | $0.05 | $21 |
| Output / 1M tokens | $1.20 | $168 |
| Cached input / 1M | $0.02 | Not listed |
| Monthly cost | $7.00 | $1,260 |
| Context window | 1M | 400K |
| Maximum output | 944K | 128K |
Input / 1M tokens
- DeepSeek V4.1 Flash
- $0.05
- GPT-5.2 Pro
- $21
Output / 1M tokens
- DeepSeek V4.1 Flash
- $1.20
- GPT-5.2 Pro
- $168
Cached input / 1M
- DeepSeek V4.1 Flash
- $0.02
- GPT-5.2 Pro
- Not listed
Monthly cost
- DeepSeek V4.1 Flash
- $7.00
- GPT-5.2 Pro
- $1,260
Context window
- DeepSeek V4.1 Flash
- 1M
- GPT-5.2 Pro
- 400K
Maximum output
- DeepSeek V4.1 Flash
- 944K
- GPT-5.2 Pro
- 128K
Benchmarks & performance
| Benchmarks & performance | DeepSeek V4.1 Flash | GPT-5.2 Pro |
|---|---|---|
| Arena Elo | Not listed | 1,255 |
| MMLU-Pro | Not listed | 82.8% |
| Intelligence Index | 39.5 | Not listed |
| Humanity’s Last Exam | 39.2% | Not listed |
| SciCode | 51.9% | Not listed |
| Output speed | 213 tokens/s | Not listed |
| Time to first token | 1.05 s | Not listed |
| DesignArena rating | 1,325 | Not listed |
| DesignArena win rate | 52.1% | Not listed |
Arena Elo
- DeepSeek V4.1 Flash
- Not listed
- GPT-5.2 Pro
- 1,255
MMLU-Pro
- DeepSeek V4.1 Flash
- Not listed
- GPT-5.2 Pro
- 82.8%
Intelligence Index
- DeepSeek V4.1 Flash
- 39.5
- GPT-5.2 Pro
- Not listed
Humanity’s Last Exam
- DeepSeek V4.1 Flash
- 39.2%
- GPT-5.2 Pro
- Not listed
SciCode
- DeepSeek V4.1 Flash
- 51.9%
- GPT-5.2 Pro
- Not listed
Output speed
- DeepSeek V4.1 Flash
- 213 tokens/s
- GPT-5.2 Pro
- Not listed
Time to first token
- DeepSeek V4.1 Flash
- 1.05 s
- GPT-5.2 Pro
- Not listed
DesignArena rating
- DeepSeek V4.1 Flash
- 1,325
- GPT-5.2 Pro
- Not listed
DesignArena win rate
- DeepSeek V4.1 Flash
- 52.1%
- GPT-5.2 Pro
- Not listed
Tools & features
| Tools & features | DeepSeek V4.1 Flash | GPT-5.2 Pro |
|---|---|---|
| Function calling | Supported | Supported |
| Structured outputs | Supported | Supported |
| JSON mode | Supported | Supported |
| Reasoning | Supported | Supported |
| Log probabilities | Supported | Not listed |
| Deterministic seed | Supported | Supported |
| Prompt caching | Supported | Not listed |
Function calling
- DeepSeek V4.1 Flash
- Supported
- GPT-5.2 Pro
- Supported
Structured outputs
- DeepSeek V4.1 Flash
- Supported
- GPT-5.2 Pro
- Supported
JSON mode
- DeepSeek V4.1 Flash
- Supported
- GPT-5.2 Pro
- Supported
Reasoning
- DeepSeek V4.1 Flash
- Supported
- GPT-5.2 Pro
- Supported
Log probabilities
- DeepSeek V4.1 Flash
- Supported
- GPT-5.2 Pro
- Not listed
Deterministic seed
- DeepSeek V4.1 Flash
- Supported
- GPT-5.2 Pro
- Supported
Prompt caching
- DeepSeek V4.1 Flash
- Supported
- GPT-5.2 Pro
- Not listed
API & availability
| API & availability | DeepSeek V4.1 Flash | GPT-5.2 Pro |
|---|---|---|
| API identifier | deepseek-v4.1-flash | gpt-5.2-pro |
| API providers | Not listed | OpenAI |
| Prices checked | Oct 8, 2026 | Oct 8, 2026 |
API identifier
- DeepSeek V4.1 Flash
deepseek-v4.1-flash- GPT-5.2 Pro
gpt-5.2-pro
API providers
- DeepSeek V4.1 Flash
- Not listed
- GPT-5.2 Pro
- OpenAI
Prices checked
- DeepSeek V4.1 Flash
- Oct 8, 2026
- GPT-5.2 Pro
- Oct 8, 2026
About the models
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash is a sparse mixture-of-experts model from DeepSeek, and the first built on the company's Causal Encoder-Decoder (CED) architecture. It activates 8B parameters on input and 16B on...
Full pricing & detailsGPT-5.2 Pro
GPT-5.2 with extra compute for the most demanding problems. No cached-input discount.
Full pricing & detailsComparison FAQ
DeepSeek V4.1 Flash: $0.05 input and $1.20 output per million tokens. GPT-5.2 Pro: $21 input and $168 output per million tokens. The cheaper choice depends on your input-to-output ratio; a model can have a lower input rate but a higher output rate.