DeepSeek V4 Flash costs $0.28 per million output tokens. GPT-5.5 costs $30 for the same million tokens. That is a 107x price difference for models that compete on the same benchmarks.
This is not a typo. It is not a promotional rate. It is the standard API price for a model that scores 79.0% on SWE-bench Verified and handles a 1M token context window. If you are building anything that calls an LLM at scale, this pricing changes the math on what is financially viable.
Here is the full breakdown of what V4 Flash costs, how it compares, and where the savings actually matter. For a deeper look at the model itself, see our DeepSeek V4 Flash complete guide.
The pricing comparison
Numbers tell the story faster than words. Here is how V4 Flash stacks up against every major frontier and mid-tier model on the market right now.
| Model | Input (per 1M tokens) | Output (per 1M tokens) | Context window |
|---|---|---|---|
| DeepSeek V4 Flash | $0.10 | $0.28 | 1M |
| DeepSeek V4 Pro | $0.50 | $2.19 | 1M |
| GPT-5.5 | $10.00 | $30.00 | 256K |
| GPT-5.4 | $2.50 | $10.00 | 128K |
| Claude Opus 4.6 | $15.00 | $75.00 | 200K |
| Gemini 3.1 Pro | $1.25 | $5.00 | 2M |
V4 Flash is not just cheaper than the flagships. It is cheaper than most models one or two tiers below them. For a full breakdown across more providers, see our AI API pricing comparison for 2026.
Cache hit pricing makes it even cheaper
DeepSeek offers a 90% discount on cached input tokens. If your prompts share a common system prompt or context prefix (and most production apps do), your effective input cost drops to $0.01 per million tokens.
| Token type | Standard price | Cache hit price | Discount |
|---|---|---|---|
| Input tokens | $0.10/1M | $0.01/1M | 90% |
| Output tokens | $0.28/1M | $0.28/1M | N/A |
With caching enabled, the input cost approaches zero. Your total cost per request becomes almost entirely the output token charge. For applications with long, repeated system prompts, this is a significant multiplier on top of already low prices.
Our guide on how to reduce LLM API costs covers caching strategies in detail.
Daily cost examples
What does this look like in practice? Here is the daily spend for a workload generating 1 million output tokens per day.
| Model | Daily cost (1M output tokens) | Monthly cost (30 days) | Annual cost |
|---|---|---|---|
| DeepSeek V4 Flash | $0.28 | $8.40 | $102.20 |
| DeepSeek V4 Pro | $2.19 | $65.70 | $799.35 |
| GPT-5.4 | $10.00 | $300.00 | $3,650.00 |
| GPT-5.5 | $30.00 | $900.00 | $10,950.00 |
| Claude Opus 4.6 | $75.00 | $2,250.00 | $27,375.00 |
At 1M tokens per day, V4 Flash costs $8.40 per month. GPT-5.5 costs $900. That is the difference between a rounding error and a real line item in your budget.
Scale that to 10M tokens per day (common for agent-based systems or batch pipelines) and V4 Flash runs $84/month while GPT-5.5 hits $9,000.
What you get for $0.28 per million tokens
Cheap means nothing if the model cannot do the work. V4 Flash is not a stripped-down budget model. It posts frontier-class scores on coding and reasoning benchmarks.
- 79.0% on SWE-bench Verified (real-world software engineering tasks)
- 91.6% on LiveCodeBench (competitive programming)
- 83.7% on AIME 2025 (advanced math reasoning)
- 1M token context window (4x larger than GPT-5.5)
- Supports tool calling, JSON mode, and FIM completions
For coding tasks specifically, V4 Flash competes with models that cost 10x to 100x more. The V4 Pro vs Flash comparison breaks down exactly where Flash matches Pro and where it falls short.
V4 Flash vs other budget models
V4 Flash is not the only cheap option. But it outperforms the other budget-tier models on most coding benchmarks while staying price-competitive.
| Model | Output price (per 1M) | SWE-bench Verified | LiveCodeBench | Context |
|---|---|---|---|---|
| DeepSeek V4 Flash | $0.28 | 79.0% | 91.6% | 1M |
| Claude Haiku 4.6 | $1.25 | 62.3% | 78.4% | 200K |
| GPT-5.4 Mini | $0.60 | 58.7% | 72.1% | 128K |
| Gemini 3.1 Flash | $0.30 | 55.2% | 69.8% | 1M |
Gemini 3.1 Flash comes closest on price at $0.30 per million output tokens, but V4 Flash leads it by over 20 points on SWE-bench. Haiku 4.6 and GPT-5.4 Mini cost more and score lower on coding tasks.
For a broader look at affordable options, see our roundup of the best budget AI models for coding in 2026.
When the savings compound
The 107x price gap matters most in high-volume scenarios where token usage multiplies quickly.
Agent loops. An AI coding agent might make 10 to 50 LLM calls per task, generating 500K+ output tokens per session. At V4 Flash pricing, a 50-call agent loop costs roughly $0.14. The same loop on GPT-5.5 costs $15. Run 100 sessions a day and you are looking at $14 vs $1,500 daily.
Batch processing. Analyzing, summarizing, or transforming large document sets can easily hit 50M+ tokens per run. V4 Flash handles that for $14. GPT-5.5 charges $1,500 for the same job.
High-volume serving. A customer-facing product serving 10,000 users, each generating 1,000 output tokens per session, produces 10M tokens daily. V4 Flash: $2.80/day. GPT-5.5: $300/day.
Multi-model pipelines. Use V4 Flash for the high-volume steps (drafting, extraction, classification) and reserve a more expensive model for the final review pass. This hybrid approach can cut total pipeline costs by 80% or more.
The DeepSeek V4 API guide walks through setting up these kinds of workflows.
Limitations to know about
V4 Flash is not the best model for every task. The price reflects real tradeoffs.
- Knowledge-heavy tasks. On benchmarks like MMLU-Pro and GPQA Diamond, V4 Flash scores several points below V4 Pro and GPT-5.5. For tasks that require deep factual recall or specialized domain knowledge, the gap is noticeable.
- Terminal-Bench performance. V4 Flash drops roughly 8 to 10 points compared to V4 Pro on Terminal-Bench, which tests complex multi-step terminal operations. If your use case involves intricate system administration or DevOps automation, Pro may be worth the premium.
- Creative writing. V4 Flash tends toward more direct, less nuanced outputs in open-ended writing tasks. It is optimized for speed and efficiency, not literary style.
- Smaller training data cutoff. V4 Flash has a slightly earlier knowledge cutoff than some competitors, which can matter for questions about very recent events.
For tasks where these limitations matter, the V4 Pro vs Flash comparison helps you decide which model fits.
FAQ
Is DeepSeek V4 Flash really 107x cheaper than GPT-5.5?
Yes. V4 Flash charges $0.28 per million output tokens. GPT-5.5 charges $30 per million output tokens. That is a 107x difference on output pricing. Input pricing shows a similar gap: $0.10 vs $10.00 per million tokens (100x). These are the standard published API rates as of April 2026.
Can V4 Flash replace GPT-5.5 for coding tasks?
For most coding tasks, yes. V4 Flash scores 79.0% on SWE-bench Verified, which is competitive with GPT-5.5. It handles code generation, debugging, refactoring, and code review at a level that works for production use. Where it falls short is on tasks requiring broad world knowledge or highly nuanced reasoning, so test it on your specific workload before switching entirely.
How do I get the cache hit discount?
Structure your API calls so that multiple requests share the same prefix (system prompt, context documents, or few-shot examples). DeepSeek automatically caches these shared prefixes and charges $0.01 per million input tokens on cache hits instead of the standard $0.10. No special configuration is needed beyond consistent prompt structure. The V4 API guide covers implementation details.