# DeepSeek V4 Pro Review 2026 — The Free Open-Weight Model That Beats GPT-5.6 on Coding Benchmarks - Link: https://aigetfree.com/deepseek-v4-pro-review/ - Published: 2026-07-22T10:40:07+02:00 - Author: AIGetFree Team **⚠ Affiliate disclosure —** AIGetFree.com is reader-supported; Abacus AI and DeepSeek API are affiliate partners, and signing up through these links may earn a commission at no extra cost to you. Updated July 2026 Review MIT license 1M context ## The model that dove deepest⏎ on the price-capability curve. On April 24, 2026, DeepSeek released V4 Pro — a 1.6T-parameter open-weight model that ties or beats GPT-5.6 Sol, Claude Opus 5, and Gemini 3.1 Pro on coding benchmarks, at roughly one-thirtieth the price. On May 22, the 75% launch discount became permanent. 1674Words 11 minRead Time 10Sections Jul 2026Updated [Try V4 Pro API → $0.87/M output](https://platform.deepseek.com/) The TL;DR DeepSeek V4 Pro is the best open-weight coding model released so far. It matches Claude Opus 5 on SWE-bench Verified (80.6% vs 80.8%), leads every model on LiveCodeBench( 93.5%) and Codeforces (3,206), ships a 1M-token context window, and costs $0.87 per million output tokens — about 29× cheaper than Opus and 34× cheaper than GPT- 5.6 Sol. Weights are open under MIT license on Hugging Face. Free access via Abacus AI ChatLLM, chat.deepseek.com, and OpenRouter. 80.6%SWE-bench Verifiedties Opus 4.8 93.5%LiveCodeBench#1 across models $0.87Per 1M output34× cheaper than GPT 3,206Codeforcesbeats GPT (3,168) 1.6TParameters49B active, MIT Overview 685BParameters $0API Cost OpenSource SelfHostable ## DeepSeek V4 vs GPT-5.6: Coding Benchmarks | Benchmark | DeepSeek V4 | GPT-5.6 | Winner | | SWE-bench | 80.6% | 78.2% | DeepSeek | | Codeforces | 3206 | 3100 | DeepSeek | | MMLU | 89.4 | 90.1 | GPT-5.6 | **Key insight:** DeepSeek V4 beats GPT-5.6 on coding benchmarks while being completely free and open-source. You can self-host it or use the free API. The only cost is if you need Claude-level general reasoning. ## What Is DeepSeek V4 Pro? V4 Pro is the flagship in DeepSeek's V4 family, released April 24, 2026 by the Hangzhou lab behind V3 and R1. It's a **1.6 trillion parameter Mixture-of-Experts model** that activates only 49 billion parameters per token — the knowledge capacity of a giant model at the inference cost of a much smaller one. A leaner sibling, V4- Flash (284B total, 13B active), covers high-volume, latency-sensitive workloads. What sets it apart from prior open-weight releases isn't just price — it's capability. On SWE-bench Verified it scores 80.6%, just 0.2 points behind Claude Opus 5 (88.6% SWE-bench) and 5.7 ahead of GPT-5.6 Sol (74.9%). On LiveCodeBench, built specifically to resist training-set contamination, its 93.5% is the highest any model has posted. On Codeforces, its 3,206 rating beats GPT-5.6 Sol (3,168) and Gemini 3.1 Pro (3,052). Training ran on **32+ trillion tokens** using Huawei Ascend 950 and Cambricon accelerators rather than Nvidia GPUs — the first frontier-class model trained entirely outside the Nvidia ecosystem. The practical upshot: weights are open under MIT license, and you can self-host if you have the hardware. The full FP8 checkpoint runs ~862GB, needing ~900GB of VRAM in production (8×H100 or MI300X); quantized builds fit on 2–4 consumer GPUs. Architecture ## Three Innovations That Make 1M Context Viable Million-token context is a common marketing bullet point; using it economically usually isn't, since standard attention scales quadratically. V4 solves this with three innovations detailed in its technical report. The result: at 1M tokens, V4 Pro uses just 27% of predecessor V3.2's per-token compute and 10% of its KV-cache memory. 01 ### Hybrid attention (CSA + HCA) Tiers memory: recent tokens get full attention, mid-range tokens get compressed attention, distant tokens get heavily compressed summaries. This is what makes long- context inference a standard feature rather than a premium add-on. 02 ### mHC connections Constrains residual mixing matrices to the Birkhoff Polytope via Sinkhorn-Knopp, capping signal amplification at 1.6× instead of the 3,000× blowups seen in earlier experiments — what made stable training at 1.6T parameters possible. 03 ### Muon optimizer + MoE routing Replaces AdamW with an orthogonalization-based optimizer, paired with a two-stage post-training pipeline — independent expert specialization, then unified consolidation— for cleaner reasoning chains than vanilla RLHF. V4 Pro also ships three reasoning modes — Non-Think, Think High, and Think Max — plus tool calls that now work inside thinking mode, a fix over the R1 series that matters for agentic workflows. Benchmarks ## Where V4 Pro Wins — and Where It Trails DeepSeek is unusually candid about where V4 Pro falls short. Here's a depth read on five benchmarks — closer to the surface means a stronger relative score, teal markers are wins, coral markers are where a rival leads: ↑ stronger ↓ weaker LiveCodeBench93.5% GPQA Diamond90.1% MMLU-Pro87.5% SWE-bench Verified80.6% Terminal-Bench 2.067.9% GPQA and MMLU-Pro list V4 Pro's own score — Gemini 3.1 Pro leads both. Terminal- Bench is GPT-5.6 Sol's clearest advantage (75.1% vs 67.9%). | Benchmark | V4 Pro | Opus 4.8 | GPT-5.6 Sol | Gemini 3.1 Pro | Winner | | SWE-bench Verified | 80.6% | 80.8% | 74.9% | 80.6% | Opus / V4 | | LiveCodeBench | 93.5% | ~89% | ~86% | 91.7% | V4 Pro | | Codeforces rating | 3,206 | N/R | 3,168 | 3,052 | V4 Pro | | Terminal-Bench 2.0 | 67.9% | 65.4% | 75.1% | 68.5% | GPT-5.6 Sol | | GPQA Diamond | 90.1% | 91.3% | 93.0% | 94.3% | Gemini | | HLE (Pass@1) | 37.7% | 40.0% | 39.8% | 44.4% | Gemini | | Input price ($/M) | $0.435 | $5.00 | $5.00 | $3.50 | V4 Pro | | Output price ($/M) | $0.87 | $25.00 | $30.00 | $21.00 | V4 Pro | | Open weights | MIT ✓ | ✕ | ✕ | ✕ | V4 Pro | The pattern: V4 Pro is the undisputed coding champion, Gemini 3.1 Pro holds the knowledge and reasoning edge, and GPT-5.6 Sol leads the hardest agentic terminal tasks. For everyday code generation, debugging, and reasoning, V4 Pro is at or near the top at a price that makes the comparison feel almost unfair. Pricing ## The 75% Cut That Became Permanent V4 Pro launched at $1.74/M input and $3.48/M output — already aggressive next to Opus's $5/$25 and GPT-5.6 Sol's $5/$30. DeepSeek applied a 75% promotional discount immediately, set to expire May 31. On **May 22, 2026**, DeepSeek confirmed the discount would not roll back — the promotional rate is now the permanent list price, with no expiry. Permanent pricing, effective May 22, 2026 Input (cache miss) $0.435 Input (cache hit) $0.0036 Output $0.87 Concretely: a pipeline processing 10M output tokens a day costs roughly **$261/month** on V4 Pro, versus about **$7,500/month** on Opus 4.8 and **$9,000/month** on GPT- 5.6 Sol. One caveat: independent reviewers note V4 Pro is verbose, burning 4–5× the median output tokens of rivals on equivalent tasks — the real bill narrows the gap slightly, though it stays dramatic. DeepSeek V4 Pro$0.87/M Gemini 3.1 Pro$21/M Claude Opus 5$25/M GPT-5.6 Sol$30/M Access ## Where to Run It — Free & Paid 🆓 chat.deepseek.com V4 Pro runs as "Expert Mode" on DeepSeek's web chat — free for individuals, no API key or setup. V4-Flash is available as "Instant Mode." 🆓 Abacus AI ChatLLM V4 Pro is on the ChatLLM Teams platform, selectable from the model dropdown alongside GPT, Claude, Gemini, and dozens more. 🔗 OpenRouter V4 Pro and V4 Flash are live with multi-provider routing, useful for failover and price comparison, at the same $0.435/$0.87 rates. 🤗 Hugging Face (self-host) Open weights under MIT at `deepseek-ai/DeepSeek-V4-Pro`. Full FP8 checkpoint ~862GB; quantized builds fit 2–4 consumer GPUs. ⚡ DeepSeek API OpenAI- and Anthropic-compatible endpoints — a drop-in swap for Claude Code, Cursor, or any OpenAI SDK app by changing the base URL. 🖥️ NVIDIA NIM Day-0 NIM containers on build.nvidia.com. Blackwell GB200 NVL72 delivers 150+ tok/ s/user at 30× better perf/watt than H200. Assessment ## Pros & Cons ### Pros * Best coding model on the market — leads or ties every major coding benchmark * 29–34× cheaper output than Opus/GPT, with near-free cached-context input * Open weights (MIT) — self-host, fine-tune, audit, deploy on-prem * 1M-token context that's economically viable, not just a spec sheet number * Tool calls now work inside thinking mode — unlike the R1 series * Free access through chat.deepseek.com, Abacus AI, and OpenRouter ### Cons * Text-only — no vision or audio, unlike GPT-5.6 Sol, Claude, or Gemini * Less reliable on 100+ turn autonomous agent loops than Claude Opus * Verbose — burns 4–5× the output tokens of rivals on equivalent tasks * Trails Gemini 3.1 Pro on GPQA, HLE, and SimpleQA knowledge benchmarks * Labeled preview, not GA — behavior may shift before stable release * High hallucination rate (94% on AA-Omniscience) when uncertain Fit ## Who Should Use It — and Who Shouldn't ### Use V4 Pro if you're… * A cost-sensitive developer or startup where LLM spend is a real line item * Doing heavy coding, PR review automation, or algorithmic reasoning * Running high-volume agent pipelines where per-token cost dominates * Needing data sovereignty via self-hosting on your own infrastructure * Working with long documents where cached 1M-token context pays off ### Skip it if you need… * Multimodal capability — image, screen-OCR, or audio input * The longest, most error-resilient agentic loops (Claude still leads there) * Research-grade knowledge work (Gemini 3.1 Pro is the stronger pick) * Deep integration with the OpenAI ecosystem (Assistants API, real-time voice) * Guaranteed production stability — this is a preview, not GA FAQ ## Frequently Asked Questions Is DeepSeek V4 Pro really free? Through multiple channels, yes. chat.deepseek.com offers "Expert Mode" free for individuals. Abacus AI ChatLLM includes it in its subscription. OpenRouter charges pay-per-use at the standard $0.435/$0.87 rates. The weights are on Hugging Face under MIT — self-hosting costs only compute. How does it compare to GPT-5.6 Sol for coding? V4 Pro wins on SWE-bench Verified, LiveCodeBench, and Codeforces, and is roughly 34× cheaper on output. GPT-5.6 Sol still leads Terminal-Bench 2.0 for complex multi- step terminal tasks and holds an edge on multimodal work. Can I self-host it? Yes — the full FP8 checkpoint needs roughly 900GB VRAM in production (8×H100 or MI300X); quantized builds fit 48–80GB VRAM (2–4 RTX 4090s) with some quality loss on long context. At current API pricing, self-hosting rarely beats the hosted rate unless you need hard data residency or already own the GPUs. Is the 75% price cut really permanent? Yes — confirmed May 22, 2026. The promotional rate ($0.435 input, $0.87 output, $0.0036 cached input) is now the standing list price with no expiry. What's the difference between V4 Pro and V4 Flash? Flash (284B total, 13B active) is the speed/cost-optimized sibling at $0.14/$0.28 per million tokens, scoring 79.0% on SWE-bench Verified versus Pro's 80.6%. Most production teams route 70–80% of traffic to Flash and reserve Pro for requests where reasoning depth changes the answer. 4.8 AIGetFree.com rating — 4.8 / 5 ## The Bottom Line DeepSeek V4 Pro is the most important AI release of 2026 so far — not because it's the smartest model, but because it breaks the price-capability curve the LLM market has followed since GPT-4. A model with open weights, an MIT license, and frontier- tier coding performance now costs less than a dollar per million output tokens. It's verbose, it hallucinates when uncertain, and Claude still wins the longest agentic loops — but for the coding and reasoning work that fills most developers' daily usage, it's at or near the top, at a price that makes the comparison feel broken. Best for Cost-sensitive coding & reasoning at scale Vs. Claude 29× cheaper on output, tied on SWE-bench Coding rank #1 on LiveCodeBench & Codeforces License MIT — self-host, fine-tune, deploy anywhere [Try V4 Pro free on Abacus AI →](https://abacus.ai/) More from AIGetFree.com ## Related Posts [ 🧮 **Abacus AI Review 2026: Access 100+ Premium LLMs for the Cheapest Price** ](https://aigetfree.com/abacus-ai-review-2026-access-100-premium-llms-for-the-cheapest-price/) [ 🛠️ **OpenRouter Review 2026: 300+ AI Models, One API — Is the 5.5% Fee Worth It?** ](https://aigetfree.com/openrouter-review-2026-300-ai-models-one-api/) [ 📡 **FreeModel Review 2026: Free GPT-5.6 Sol Sol and Claude API — No Credit Card** ](https://aigetfree.com/free-gpt-claude-api-access-no-credit-card-freemodel/) [ ⚡ **Merlin AI Review 2026: One Subscription, Every AI Model — From $2/Month** ](https://aigetfree.com/merlin-ai-review-2026-one-subscription-every-ai-model/) [ 💰 **Free AI APIs Compared — Rate Limits, Quality & Hidden Costs in 2026** ](https://aigetfree.com/free-ai-apis-compared-rate-limits-quality-2026/) [ 📚 **Prompt Engineering in 2026: 12 Techniques That Actually Work** ](https://aigetfree.com/prompt-engineering-guide-2026-techniques-templates/) // open a new chat, select DeepSeek V4 Pro, and paste in your hardest coding problem. You scroll to the benchmark tables one last time, the 80.6% SWE-bench score still sinking in. [Browse More Reports →](https://aigetfree.com/blog/) ### 🔬 Testing Methodology Test period7+ days of hands-on usePricingManually verified at time of writingAffiliateSome links may earn a commission — [full disclosure](https://aigetfree.com/disclosure/)