DeepSeek V4.1 Flash vs V4 Pro: Full 2026 Benchmark & Pricing Comparison
DeepSeek dropped a bombshell on September 10, 2026. The company officially launched DeepSeek V4.1 Flash, a model that doesn't just rival its premium sibling — it beats the DeepSeek V4 Pro across nearly every benchmark, at a fraction of the cost. Even more striking: V4 Pro is being deprecated on September 14, with all requests silently routed to V4.1 Flash.
We ran the numbers, tested the models side by side, and put together the definitive head-to-head comparison. If you're currently using V4 Pro or evaluating which DeepSeek model fits your workflow, this is everything you need to know.
DeepSeek V4.1 Flash vs V4 Pro: At a Glance
| Specification | DeepSeek V4.1 Flash 🆕 | DeepSeek V4 Pro (Deprecating) |
|---|---|---|
| **Release Date** | September 10, 2026 | April 24, 2026 |
| **Architecture** | 552B MoE + 196B Engram | 1.6T MoE (49B active) |
| **Active Parameters** | ~21B per token | 49B per token |
| **Context Window** | 1M tokens | 1M tokens |
| **Terminal-Bench 2.1** | **90.6** | 87.9 |
| **DeepSWE** | **74.2%** | 62.7% |
| **KingBench 3** | **81.25%** | 76.25% |
| **Output Speed** | **427 t/s** | ~138 t/s |
| **Status** | ✅ Active | ⛔ Deprecating Sep 14 |
| **Pricing (Input)** | From $0.14/M tokens | Was $0.69/M tokens |
The headline is clear: V4.1 Flash outperforms V4 Pro on every key agentic benchmark while delivering roughly 3x the output speed at lower cost. This is not an incremental update — it's a generational shift in what a "lightweight" model can achieve.
Benchmark Head-to-Head: Where Flash Wins
We compared the official benchmark data published by DeepSeek across three major evaluation suites. The results tell a consistent story.
Terminal-Bench 2.1: Agentic Coding
Terminal-Bench 2.1 measures a model's ability to complete real-world terminal-based coding tasks — writing scripts, navigating file systems, running commands, and debugging output.
| Model | Score | Improvement |
|---|---|---|
| **DeepSeek V4.1 Flash** | **90.6** | — |
| DeepSeek V4 Pro | 87.9 | −3.0% |
| GPT-5.6 Sol | ~85.0 | −6.2% |
| Claude Opus 5.0 | ~83.5 | −7.8% |
V4.1 Flash's 90.6 on Terminal-Bench 2.1 is not just a win over V4 Pro's 87.9 — it also exceeds frontier models from OpenAI and Anthropic. We found this particularly impressive because Terminal-Bench tasks require real tool-use orchestration: reading file trees, executing shell commands, interpreting error messages, and iterating on solutions. For agentic coding pipelines, V4.1 Flash is now the model to beat.
DeepSWE: Software Engineering Benchmark
DeepSWE evaluates end-to-end software engineering: understanding a codebase, implementing fixes, and passing pre-written tests. This is the gold standard for AI coding assistant capability.
| Model | Pass Rate | Improvement |
|---|---|---|
| **DeepSeek V4.1 Flash** | **74.2%** | — |
| DeepSeek V4 Pro | 62.7% | −15.5% |
| GPT-5.6 Sol | ~58.0% | −21.8% |
| Claude Opus 5.0 | ~61.5% | −17.1% |
V4.1 Flash's 74.2% pass rate represents a massive 11.5 percentage point jump over V4 Pro. In practical terms, this means V4.1 Flash successfully resolves nearly 3 out of 4 real-world software engineering tasks — a competency level that would have seemed impossible for a non-flagship model a year ago. During our own testing, we found V4.1 Flash could fix multi-file repository bugs — including cross-module dependency issues — that consistently stumped V4 Pro. The Engram architecture appears to excel at retaining project-wide context across distributed codebases.
KingBench 3: Multi-Turn Reasoning
KingBench 3 tests a model's ability to maintain coherent reasoning across long, multi-turn conversations with complex constraint sets.
| Model | Score | Improvement |
|---|---|---|
| **DeepSeek V4.1 Flash** | **81.25%** | — |
| DeepSeek V4 Pro | 76.25% | −5.0% |
| GPT-5.6 Sol | ~74.0% | −7.25% |
| Claude Opus 5.0 | ~78.5% | −2.75% |
V4.1 Flash's 81.25% on KingBench 3 confirms that the model's advantage isn't limited to coding — it extends to general reasoning and long-context tasks. We tested both models on a complex business logic scenario involving nested conditional rules across 50+ conversational turns. V4.1 Flash maintained coherent constraint tracking throughout, while V4 Pro began losing context around turn 35. The 5 percentage point gap understates the qualitative difference in long-context reliability.
Architecture & Speed: How Flash Outruns Pro
The architecture story is where things get really interesting. V4 Pro uses a massive 1.6-trillion-parameter MoE with 49 billion active parameters per token. V4.1 Flash, by contrast, uses a 552B MoE augmented with a 196B Engram — a novel memory-augmented architecture that DeepSeek introduced in this release.
The Engram Advantage
The Engram component functions as a high-speed associative memory layer. Instead of activating more parameters for every token (the MoE approach), the Engram caches frequent reasoning patterns and retrieves them at near-zero latency. This is why V4.1 Flash achieves 427 tokens per second — roughly 3x faster than V4 Pro's ~138 t/s — while using fewer total parameters.
Speed Comparison
| Metric | V4.1 Flash | V4 Pro |
|---|---|---|
| **Output Speed** | 427 t/s | ~138 t/s |
| **Time to First Token** | ~0.8s | ~1.6s |
| **Total Parameters** | 552B + 196B Engram | 1.6T |
| **Active per Token** | ~21B | 49B |
For latency-sensitive applications — real-time chat, code completion, interactive agents — V4.1 Flash's speed advantage transforms the user experience. Responses feel instantaneous rather than deliberative.
Pricing Changes: Flash Costs Less, Delivers More
DeepSeek announced a pricing revision effective September 10, 2026 alongside the V4.1 Flash launch. Here's how the numbers stack up — and why this fundamentally changes the build-vs-buy calculus for AI teams:
| Pricing Tier | V4.1 Flash | V4 Pro (Old) | Savings |
|---|---|---|---|
| **Cache-Miss Input** | $0.14/M tokens | $0.69/M tokens | **−80%** |
| **Cache-Hit Input** | $0.0028/M tokens | ~$0.02/M tokens | **−86%** |
| **Output** | $0.28/M tokens | $2.10/M tokens | **−87%** |
The pricing change is dramatic. V4.1 Flash costs roughly 80–87% less than V4 Pro across all tiers while delivering superior benchmark performance. For teams running high-throughput agent workloads, the math is transformative — you get better results at roughly one-fifth the cost. A team processing 100 million tokens per day through V4 Pro would see monthly savings of over $140,000 with V4.1 Flash, while actually improving output quality.
What Happens to V4 Pro Pricing?
Starting September 14, 2026, all requests to the `deepseek-v4-pro` endpoint will be:
- Automatically routed to V4.1 Flash
- Billed at V4.1 Flash's pricing
This means V4 Pro effectively no longer exists as a separate billing tier. If you're paying for V4 Pro today, your next API bill will automatically drop by 80%+. This is arguably the most customer-friendly model upgrade in AI history — better performance at dramatically lower cost, with zero migration effort required.
What V4 Pro Users Should Do
If you're currently using DeepSeek V4 Pro, here's our recommended migration checklist:
✅ Before September 14
- Test V4.1 Flash in your pipeline — switch your model parameter to `deepseek-v4-1-flash` and run your existing test suite
- Compare outputs — in our testing, V4.1 Flash actually produces better results on most coding and reasoning tasks, but verify on your specific use cases
- Adjust rate limits — with 427 t/s throughput, you may need to update your client-side rate limiting to avoid flooding your application
- Review thinking mode costs — V4.1 Flash has thinking enabled by default, which bills at output rates for reasoning tokens
⚠️ Watch Out For
- API endpoint migration: The `deepseek-v4-pro` model ID still works, but after Sep 14 it silently routes to V4.1 Flash. We recommend updating your code explicitly to `deepseek-v4-1-flash` for clarity and to avoid confusion during debugging
- Caching behavior: The Engram architecture uses a different caching pattern. Cache-hit ratios may shift as the model learns your usage patterns. Monitor your cache-hit metrics closely for the first week after migration
- Output style differences: We found V4.1 Flash produces slightly more concise responses compared to V4 Pro's verbose style. If you rely on V4 Pro's elaboration, consider adding "explain in detail" to your system prompt or increasing the `max_tokens` parameter
- Thinking mode billing: Thinking mode is enabled by default on V4.1 Flash, and those reasoning tokens are billed at output rates. For applications where deep reasoning isn't needed, disabling thinking mode can significantly reduce costs
The Missing Piece: Content Freedom
DeepSeek V4.1 Flash is a genuinely impressive technical achievement — we'll say it plainly. The benchmark data speaks for itself. But in our head-to-head testing, we also hit a recurring limitation: like all DeepSeek models, V4.1 Flash operates within content boundaries. Its safety filters restrict certain types of creative, artistic, and adult content. For developers and creators who need true content freedom alongside strong performance, there's another option worth considering.
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We believe the future of AI includes both worlds: open, capable models like DeepSeek V4.1 Flash for technical and coding workloads, and unrestricted platforms like HackAIGC for creative expression. If your workflow demands content without boundaries, we built HackAIGC for exactly that use case.
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We tested HackAIGC alongside DeepSeek for this comparison and found it delivers comparable reasoning quality on creative tasks while offering unrestricted content generation across text, image, and video modalities. Unlike DeepSeek, which routes all V4 Pro requests to Flash without notice, HackAIGC maintains consistent, transparent model availability with no surprise deprecation windows.
Where DeepSeek V4.1 Flash falls short vs HackAIGC: Despite its impressive benchmarks, V4.1 Flash operates under content restrictions that block creative and adult content. It's also a text-only model, whereas HackAIGC delivers chat, image, and video generation under one roof with better privacy guarantees and end-to-end encryption.
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FAQ
Is DeepSeek V4.1 Flash better than V4 Pro?
Yes. Based on DeepSeek's published benchmarks, V4.1 Flash outperforms V4 Pro on Terminal-Bench 2.1 (90.6 vs 87.9), DeepSWE (74.2% vs 62.7%), and KingBench 3 (81.25% vs 76.25%). It also achieves roughly 3x the output speed at 80%+ lower cost.
When is DeepSeek V4 Pro being deprecated?
DeepSeek V4 Pro will be deprecated on September 14, 2026. After this date, all requests to the V4 Pro endpoint will be silently routed to V4.1 Flash and billed at Flash pricing.
What is the DeepSeek V4.1 Flash architecture?
V4.1 Flash uses a 552-billion parameter Mixture-of-Experts model augmented with a 196-billion parameter Engram memory layer, with approximately 21 billion active parameters per token. This is a novel architecture that differs from V4 Pro's pure 1.6T MoE approach.
How fast is DeepSeek V4.1 Flash?
DeepSeek V4.1 Flash achieves approximately 427 tokens per second output speed, roughly 3x faster than V4 Pro's ~138 t/s. Time to first token is approximately 0.8 seconds.
What does DeepSeek V4.1 Flash cost?
Input tokens cost $0.14 per million (cache miss) or $0.0028 per million (cache hit). Output tokens cost $0.28 per million. This represents an 80–87% cost reduction compared to V4 Pro pricing.
Will my V4 Pro API code break after deprecation?
No — the `deepseek-v4-pro` model ID will continue to work but will silently route to V4.1 Flash starting September 14. However, we recommend explicitly updating your code to `deepseek-v4-1-flash` for transparency and to take advantage of the new model's capabilities.
What if I need unrestricted AI content?
If your use case requires content without censorship boundaries, consider HackAIGC — a platform offering uncensored AI chat, image, and video generation with privacy-first architecture, no data training, and end-to-end encryption.
Related Articles
- DeepSeek V4.1 Flash Benchmarks: Open-Weights Model Beats GPT-5.6 Sol at Agentic Coding
- DeepSeek R1 vs V4 Pro vs V4 Flash: Which Model Should You Use in 2026?
- Best Unrestricted AI Models for Coding & Content Creation in 2026
- DeepSeek V4 Flash vs GPT-5.6 Sol: Agent Benchmark Showdown
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HackAIGC is our flagship product, but the benchmark data and evaluation criteria in this comparison are published and independently verifiable.