DeepSeek-V4がOpus 4.7やGPT-5.5の1/6のコストでほぼ最先端の知能を搭載して登場

The whale has resurfaced. DeepSeek, the Chinese AI startup offshoot of High-Flyer Capital Management quantitative analysis firm, became a near-overnight sensation globally in January 2025 with the release of its open source R1 model that matched proprietary U.S. giants. It's been an epoch in AI since then, and while DeepSeek has released several updates to that model and its other V3 series, the international AI and business community has been largely waiting with baited breath for the follow-up to the R1 moment. Now it's arrived with last night's release of DeepSeek-V4 , a 1.6-trillion-parameter Mixture-of-Experts (MoE) model available free under commercially-friendly open source MIT License, which nears — and on some benchmarks, surpasses — the performance of the world’s most advanced closed-source systems at approximately 1/6th the cost over the application programming interface (API). This release—which DeepSeek AI researcher Deli Chen described on X as a "labor of love" 484 days after the launch of V3—is being hailed as the "second DeepSeek moment". As Chen noted in his post, "AGI belongs to everyone". It's available now on AI code sharing community Hugging Face and through DeepSeek's API . Frontier-class AI gets pushed into a lower price band The most immediate impact of the DeepSeek-V4 launch is economic. The corrected pricing table shows DeepSeek is not pricing its new Pro model at near-zero levels, but it is still pushing high-end model access into a far lower cost tier than the leading U.S. frontier models. DeepSeek-V4-Pro is priced through its API at $1.74 USD per 1 million input tokens on a cache miss and $3.48 per million output tokens . That puts a simple one-million-input, one-million-output comparison at $5.22 . With cached input, the input price drops to $0.145 per million tokens , bringing that same blended comparison down to $3.625 . That is dramatically cheaper than the current premium pricing from OpenAI and Anthropic. GPT-5.5 is priced at $5.00 per million input tokens and $30.00 per million output tokens , for a combined $35.00 in the same simple comparison. Claude Opus 4.7 is priced at $5.00 input and $25.00 output , for a combined $30.00 . Model Input Output Total Cost Source Grok 4.1 Fast $0.20 $0.50 $0.70 xAI MiniMax M2.7 $0.30 $1.20 $1.50 MiniMax Gemini 3 Flash $0.50 $3.00 $3.50 Google Kimi-K2.5 $0.60 $3.00 $3.60 Moonshot MiMo-V2-Pro (≤256K) $1.00 $3.00 $4.00 Xiaomi MiMo GLM-5 $1.00 $3.20 $4.20 Z.ai GLM-5-Turbo $1.20 $4.00 $5.20 Z.ai DeepSeek-V4-Pro $1.74 $3.48 $5.22 DeepSeek GLM-5.1 $1.40 $4.40 $5.80 Z.ai Claude Haiku 4.5 $1.00 $5.00 $6.00 Anthropic Qwen3-Max $1.20 $6.00 $7.20 Alibaba Cloud Gemini 3 Pro $2.00 $12.00 $14.00 Google GPT-5.2 $1.75 $14.00 $15.75 OpenAI GPT-5.4 $2.50 $15.00 $17.50 OpenAI Claude Sonnet 4.5 $3.00 $15.00 $18.00 Anthropic Claude Opus 4.7 $5.00 $25.00 $30.00 Anthropic GPT-5.5 $5.00 $30.00 $35.00 OpenAI GPT-5.4 Pro $30.00 $180.00 $210.00 OpenAI On standard, cache-miss pricing, DeepSeek-V4-Pro comes in at roughly one-seventh the cost of GPT-5.5 and about one-sixth (1/6th) the cost of Claude Opus 4.7 . With cached input, the gap widens: DeepSeek-V4-Pro costs about one-tenth as much as GPT-5.5 and about one-eighth as much as Claude Opus 4.7 . The more extreme near-zero story belongs to DeepSeek-V4-Flash , not the Pro model. Flash is priced at $0.14 per million input tokens on a cache miss and $0.28 per million output tokens , for a combined $0.42 . With cached input, that drops to $0.308 . In that case, DeepSeek’s cheaper model is more than 98% below GPT-5.5 and Claude Opus 4.7 in a simple input-plus-output comparison, or nearly 1/100th the cost — though the performance dips significantly. DeepSeek is compressing advanced model economics into a much lower band, forcing developers and enterprises to revisit the cost-benefit calculation around premium closed models. For companies running large inference workloads, that price gap can change what is worth automating. Tasks that look too expensive on GPT-5.5 or Claude Opus 4.7 may become economically viable on DeepSeek-V4-Pro, and even more so on DeepSeek-V4-Flash. The launch does not make intelligence free, but it does make the market harder for premium providers to defend on performance alone. Benchmarking the frontier: DeepSeek-V4-Pro gets close, but GPT-5.5 and Opus 4.7 still lead on most shared tests DeepSeek-V4-Pro-Max is best understood as a major open-weight leap, not a clean across-the-board defeat of the newest closed frontier systems. The model’s strongest benchmark claims come from DeepSeek’s own comparison tables, where it is shown against GPT-5.4 xHigh, Claude Opus 4.6 Max and Gemini 3.1 Pro High and bests them on several tests, including Codeforces and Apex Shortlist. But that is not the same as a head-to-head against OpenAI’s newer GPT-5.5 or Anthropic’s newer Claude Opus 4.7. Looking only at DeepSeek-V4 versus the latest proprietary models, the picture is more restrained. On this shared set, GPT-5.5 and Claude Opus 4.7 still lead most categories. DeepSeek-V4-Pro-Max’s best showing is on BrowseComp , the benchmark measuring agentic AI web browsing prowess (especially highly containerized information), where it scores 83.4%, narrowly behind GPT-5.5 at 84.4% and ahead of Claude Opus 4.7 at 79.3%. On Terminal-Bench 2.0, DeepSeek scores 67.9% , close to Claude Opus 4.7’s 69.4% , but far behind GPT-5.5’s 82.7% . Benchmark DeepSeek-V4-Pro-Max GPT-5.5 GPT-5.5 Pro, where shown Claude Opus 4.7 Best result among these GPQA Diamond 90.1% 93.6% — 94.2% Claude Opus 4.7 Humanity’s Last Exam, no tools 37.7% 41.4% 43.1% 46.9% Claude Opus 4.7 Humanity’s Last Exam, with tools 48.2% 52.2% 57.2% 54.7% GPT-5.5 Pro Terminal-Bench 2.0 67.9% 82.7% — 69.4% GPT-5.5 SWE-Bench Pro / SWE Pro 55.4% 58.6% — 64.3% Claude Opus 4.7 BrowseComp 83.4% 84.4% 90.1% 79.3% GPT-5.5 Pro MCP Atlas / MCPAtlas Public 73.6% 75.3% — 79.1% Claude Opus 4.7 The shared academic-reasoning results favor the closed models: On GPQA Diamond, DeepSeek-V4-Pro-Max scores 90.1%, while GPT-5.5 reaches 93.6% and Claude Opus 4.7 reaches 94.2%. On Humanity’s Last Exam without tools, DeepSeek scores 37.7%, behind GPT-5.5 at 41.4%, GPT-5.5 Pro at 43.1% and Claude Opus 4.7 at 46.9%. With tools enabled, DeepSeek rises to 48.2%, but still trails GPT-5.5 at 52.2%, GPT-5.5 Pro at 57.2% and Claude Opus 4.7 at 54.7%. The agentic and software-engineering results are more mixed, but they still show DeepSeek-V4-Pro-Max trailing GPT-5.5 and Opus 4.7. On Terminal-Bench 2.0, DeepSeek’s 67.9% is competitive with Claude Opus 4.7’s 69.4%, but GPT-5.5 is much higher at 82.7%. On SWE-Bench Pro, DeepSeek’s 55.4% trails GPT-5.5 at 58.6% and Claude Opus 4.7 at 64.3%. On MCP Atlas, DeepSeek’s 73.6% is slightly behind GPT-5.5 at 75.3% and Claude Opus 4.7 at 79.1%. BrowseComp is the standout: DeepSeek’s 83.4% beats Claude Opus 4.7’s 79.3% and nearly matches GPT-5.5’s 84.4%, though GPT-5.5 Pro’s 90.1% remains well ahead. So ultimately, DeepSeek-V4-Pro-Max does not appear to dethrone GPT-5.5 or Claude Opus 4.7 on the benchmarks that can be directly compared across the companies’ published tables. But it gets close enough on several of them — especially BrowseComp, Terminal-Bench 2.0 and MCP Atlas — that its much lower API pricing becomes the headline. In practical terms, DeepSeek does not need to win every leaderboard row to matter. If it can deliver near-frontier performance on many enterprise-relevant agent and reasoning tasks at roughly one-sixth to one-seventh the standard API cost of GPT-5.5 or Claude Opus 4.7, it still forces a major rethink of the economics of advanced AI deployment. DeepSeek-V4-Pro-Max is clearly the strongest open-weight model in the field right now, and it is unusually close to frontier closed systems on several practical benchmarks. While GPT-5.5 and Claude Opus 4.7 still retain the lead in most direct head-to-head comparisons across the company's benchmark charts, DeepSeek V4 Pro gets close while being dramatically cheaper and openly available. A big jump from DeepSeek V3.2 To understand the magnitude of this release, one must look at the performance gains of the base models. DeepSeek-V4-Pro-Base represents a significant advancement over the previous generation, DeepSeek-V3.2-Base. In World Knowledge, V4-Pro-Base achieved 90.1 on MMLU (5-shot) compared to V3.2’s 87.8, and a massive jump on MMLU-Pro from 65.5 to 73.5. The improvement in high-level reasoning and verified facts is even more pronounced: on SuperGPQA, V4-Pro-Base reached 53.9 compared to V3.2's 45.0, and on the FACTS Parametric benchmark, it more than doubled its predecessor's performance, jumping from 27.1 to 62.6. Simple-QA verified scores also saw a dramatic rise from 28.3 to 55.2. The Long Context capabilities have also been refined. On LongBench-V2, V4-Pro-Base scored 51.5, significantly outpacing the 40.2 achieved by V3.2-Base. In Code and Math, V4-Pro-Base reached 76.8 on HumanEval (Pass@1), up from 62.8 on V3.2-Base. These numbers underscore that DeepSeek has not just optimized for inference cost, but has fundamentally improved the intelligence density of its base architecture. The efficiency story is equally compelling for the Flash variant. DeepSeek-V4-Flash-Base, despite utilizing a substantially smaller number of parameters, outperforms the larger V3.2-Base across wide benchmarks, particularly in long-context scenarios. A new information 'traffic controller,' Manifold-Constrained Hyper-Connections (mHC) DeepSeek’s ability to offer these prices and performance figures is rooted in radical architectural innovations detailed in its technical report also released today, " Towards Highly Efficient Million-Token Context Intelligence ." The standout technical achievement of V4 is its native one-million-token context window. Historically, maintaining such a large context required massive memory (the key values or KV cache). DeepSeek solved this by introducing a Hybrid Attention Architecture that co