DeepSeek V4 Pro — a flagship for deep reasoning and large-scale code engineering
Million-token context
Think High / Max available
Open weights for comparison
Suited to long coding jobs
What is DeepSeek V4 Pro?
DeepSeek V4 Pro is DeepSeek’s V4 flagship from April 2026—built for hard-constraint reasoning, cross-file repo coding, and multi-step tool jobs that need to keep moving for a long time. Versus DeepSeek V3.2, long context is more efficient, and you can open Think High / Max for deeper thinking; for faster, lighter loads in the same family, look at V4 Flash. On iMini Agent, pick DeepSeek V4 Pro to use it.
Vendor
DeepSeek
Released
Apr 2026
Context
1M tokens
Max output
Per platform limit
Deep thinking
Think High / Max available
Best for
Hard reasoning & full-repo coding
What’s new versus DeepSeek V3.2
Long-context efficiency, Think depth, and long-job completion—the changes to check before making it your primary pick.
More efficient long context
Hybrid attention makes reasoning over million-token materials more affordable—suited to full-repo and long-spec jobs.
Think High / Max
Hard tasks can open a larger thinking budget; simple tasks can skip thinking—so depth isn’t one-size-fits-all.
More complete long coding jobs
Better suited to cross-file coding and multi-step tool work—holding the goal through to a deliverable result.
Open weights for comparison
Comparable open weights and preview materials support research and private evaluation; online, you can still use it directly on iMini.
Official evaluations
Scores and architecture figures from the DeepSeek-V4 technical report (arXiv:2606.19348)—covering knowledge reasoning, agents, long-text retrieval, and a human comparison against Claude Opus 4.6.
Main score and efficiency chart. SWE Verified 80.6%, Terminal Bench 2.0 67.9%, Codeforces 3206; the right side shows vs V3.2, Pro cuts FLOPs ~3.7× and KV Cache ~9.5× at 1M context—read ability and long-context cost together.
Five-dimension human ratings: task completion, instruction following, content quality, layout aesthetics, and overall. DeepSeek-V4-Pro-Max overall 86.52 vs Opus-4.6-Max 84.06; instruction following is slightly lower (87.76 vs 88.88).
Win-rate comparison against Claude Opus 4.6. Complements the absolute scores: which side wins more often in paired judgments.
MRCR 8-needle long-context retrieval. Pro-Max stays above 0.90 at most points before 128K and is still 0.59 at 1M; Flash-Max follows the same shape but lower. Direct evidence that million-token context is usable—not just a labeled window.
How scores move across Non-Think / High / Max. Shows V4-Pro is not a single number: raise intensity and agent/hard-problem scores move with it—pick the level by task at deploy time.
Interleaved thinking and tool-call dialogue structure. The report uses it to show how V4 keeps thinking blocks and tool results across turns—mechanism for agent ability, not one benchmark score.
Three common workflows
DeepSeek V4 Pro fits hard-constraint reasoning, full-repo coding, and multi-step jobs that need to run for a long time.
Full-repo coding
For cross-file features, refactors, and test fixes. Open higher Think when needed.
Hard-constraint reasoning
For math, logic, and derivation under explicit rules. Keep steps checkable.
Long multi-step tool jobs
For continuous tool calls that advance step by step to a result. Agree on success criteria and stop conditions first.
How to choose vs DeepSeek V4 Flash and Gemini 3.1 Pro
All three are modern primary picks. The gap is mainly Pro vs Flash ceiling and product stack.
Criterion
DeepSeek V4 Pro
DeepSeek V4 Flash
Gemini 3.1 Pro
Context
1M tokens
1M tokens
1M tokens
Reasoning
Think High / Max
Lighter · Flash-Max can approach
Pro-tier thinking, adjustable
Coding & agents
Complex coding & long-horizon agents
High-throughput coding & chat
Multimodal Pro coding
Long-horizon tools
Long-horizon automation posture
Faster short-to-mid agents
More complete multimodal orchestration
Speed posture
Favors finish quality
Favors throughput
Favors precision & modalities
Prefer when
Code & hard-reasoning flagship
High-turnaround DeepSeek work
Hard work on the Google stack
Related articles
Guides, comparisons, and workflows for this model.