High-volume, cost-sensitive work
Choose DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 costs 15% less on a typical mix of three input tokens for every output token ($0.15 against $0.18 per million tokens).
Choose DeepSeek V4 Flash 0731 for high-volume, cost-sensitive work, writing and code generation, reused prompts and documents (RAG, agents) and the latest release. Choose GPT-4.1 nano for images as input.
Each answer below comes straight from the published prices, specifications and independent benchmark results.
Choose DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 costs 15% less on a typical mix of three input tokens for every output token ($0.15 against $0.18 per million tokens).
Choose DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 charges $0.24 per million output tokens, against $0.40 for GPT-4.1 nano. Output dominates the bill when answers are long.
Choose DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 charges $0.02 per million cached input tokens, against $0.03 for GPT-4.1 nano.
Choose GPT-4.1 nano
GPT-4.1 nano accepts images as input; DeepSeek V4 Flash 0731 does not.
Choose DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 is the newer release (Jul 31, 2026, against Apr 14, 2025 for GPT-4.1 nano).
| Volume | DeepSeek V4 Flash 0731 | GPT-4.1 nano | Difference |
|---|---|---|---|
| Prototype: 1M input, 250K output tokens | $0.18 | $0.20 | DeepSeek V4 Flash 0731, $0.02 less |
| Production: 20M input, 5M output tokens | $3.57 | $4.00 | DeepSeek V4 Flash 0731, $0.43 less |
| Scale: 500M input, 100M output tokens | $83.30 | $90.00 | DeepSeek V4 Flash 0731, $6.70 less |
Standard rates, without caching or batch discounts. Volumes are per month.
| DeepSeek V4 Flash 0731 | GPT-4.1 nano | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Category | Language models | Language models |
| Status | Generally available | Deprecated |
| Headline price | $0.1488 / 1M tokens | $0.175 / 1M tokens |
| Input | $0.119 / 1M tokens | $0.10 / 1M tokens |
| Output | $0.238 / 1M tokens | $0.40 / 1M tokens |
| Cached input | $0.0238 / 1M tokens | $0.025 / 1M tokens |
| Context window | 1.05M | 1.05M |
| Max output | 944k | 33k |
| Released | Jul 31, 2026 | Apr 14, 2025 |
| Knowledge cutoff | No data | June 2024 |
| Licence | Proprietary | Proprietary |
| Capabilities | Reasoning, Tool calling, Structured output, Prompt caching | Vision, Tool calling, Structured output, Prompt caching, Batch API |
| API model id | deepseek-v4-flash-0731 | gpt-4.1-nano |
Leader in bold.
| Benchmark | DeepSeek V4 Flash 0731 | GPT-4.1 nano |
|---|---|---|
| Terminal-Bench Hard | No data | 3.8% |
| GPQA Diamond | No data | 51.2% |
| Humanity's Last Exam | No data | 3.8% |
| IFBench (instruction following) | No data | 32% |
| τ²-Bench (agentic tool use) | No data | 17.3% |
| MMMU-Pro (visual reasoning) | No data | 40.1% |
| Long-context reasoning (AA-LCR) | No data | 20.3% |
Independent evaluations by Artificial Analysis, run the same way for every model, at the reasoning setting shown. Percentages unless marked as an index.
Choose DeepSeek V4 Flash 0731 for high-volume, cost-sensitive work, writing and code generation, reused prompts and documents (RAG, agents) and the latest release. Choose GPT-4.1 nano for images as input.
DeepSeek V4 Flash 0731 is 15% cheaper than GPT-4.1 nano at the headline price ($0.1488 / 1M tokens vs $0.175 / 1M tokens).
DeepSeek V4 Flash 0731 reads more per request: 1.05M tokens against 1.05M.
DeepSeek V4 Flash 0731 is made by DeepSeek and GPT-4.1 nano by OpenAI. DeepSeek V4 Flash 0731 is 15% cheaper than GPT-4.1 nano at the headline price ($0.1488 / 1M tokens vs $0.175 / 1M tokens). DeepSeek V4 Flash 0731 reads more per request: 1.05M tokens against 1.05M. DeepSeek V4 Flash 0731 is the newer model (Jul 31, 2026, against Apr 14, 2025). Only DeepSeek V4 Flash 0731 offers reasoning. Only GPT-4.1 nano offers vision, batch api.
At 20 million input and 5 million output tokens a month, DeepSeek V4 Flash 0731 costs $3.57 and GPT-4.1 nano costs $4.00, at standard rates.
DeepSeek V4 Flash 0731 was released on Jul 31, 2026 and GPT-4.1 nano on Apr 14, 2025.