Mapping the DeepSeek V4 Evaluation Suite: A Field Guide to 2026 LLM Benchmarks

When DeepSeek dropped V4-Pro on April 24, 2026, the technical report packed in roughly sixteen distinct benchmarks across coding, reasoning, knowledge, long-context, and agentic tasks. If you’ve scrolled through the scorecard and wondered what half of those acronyms actually test, this is the field guide.

Why So Many Benchmarks Now?

A year ago, a frontier report might cite MMLU, HumanEval, and GSM8K and call it a day. In 2026 that’s not enough — those three are saturated. Artificial Analysis removed MMLU-Pro, AIME 2025, and LiveCodeBench from Intelligence Index v4.0 in January precisely because frontier models had pushed them past the point where scores could discriminate.

So new benchmarks proliferate along two axes: harder reasoning (HLE, CritPt, Putnam) and longer-horizon agentic work (SWE-bench Pro, Terminal-Bench, Toolathlon, MCPAtlas).

Coding & Software Engineering

LiveCodeBench

Contamination-free competitive programming, continuously updated from LeetCode, AtCoder, and Codeforces. Each problem carries a release date, so you can evaluate a model only on problems published after its training cutoff. V4-Pro-Max scores 93.5 — a new open-model high.

Codeforces (CodeElo)

Unlike LiveCodeBench, CodeElo hits the actual Codeforces judge via a submission bot. Zero false positives, supports special judges, and yields a human-comparable Elo rating. V4-Pro-Max lands at 3206, ranking around #23 among human contestants.

SWE-bench Verified & SWE-bench Pro

SWE-bench Verified (OpenAI-curated subset of 500 GitHub issue→PR tasks) is now saturated — most frontier models clear 70%. SWE-bench Pro is Scale AI’s answer: 1,865 long-horizon tasks across 41 repos, averaging 107 lines of code across 4 files per fix. Split into public (731), held-out (858), and commercial (276 from enterprise startups) subsets, using copyleft licenses to resist training contamination.

V4-Pro-Max: 80.6 on Verified, 55.4 on Pro. The gap is the story — Pro is where long-horizon capability gets graded honestly.

Terminal-Bench Hard & Terminal-Bench 2.0

Stanford + Laude Institute. Agents get a real terminal inside Docker and must compile code, train models, configure servers. V4-Pro-Max: 67.9 on 2.0 — still trailing the closed frontier here.

SciCode

80 primary problems decomposed into 338 sub-problems, drawn from physicists’ and chemists’ actual research scripts. Tests knowledge recall + reasoning + code synthesis simultaneously across 6 scientific domains.

MCPAtlas & Toolathlon

Newer agent-coding benchmarks. V4-Pro-Max hits 73.6 on MCPAtlas (second only to Opus-4.6-Max at 73.8) and 51.8 on Toolathlon — ahead of Gemini-3.1-Pro here.

Reasoning & Math

GPQA Diamond

198 graduate-level science MCQs, filtered so that PhD experts score ~65% but non-experts with Google score only 34%. Now saturated at the top (Gemini 3.1 Pro 94.1%, Claude Opus 4.7 94.2%).

Humanity’s Last Exam (HLE)

2,500 expert-vetted questions across math, sciences, and humanities. Released January 2025 by CAIS + Scale AI as “the final closed-ended academic exam.” Top models still sit in the low 40s.

CritPt

71 unpublished physics research problems from 50+ physicists. The discriminator where Diamond no longer can discriminate — even GPT-5.4 Pro xhigh tops out at 30%, baseline models around 4%.

Putnam-2025

Formal theorem proving on the Putnam competition, evaluated via a Lean/Isabelle pipeline. V4-Pro-Max: a clean 120/120.

Knowledge & Factuality

SimpleQA-Verified

Epoch AI’s curated 1,000-question subset of OpenAI’s SimpleQA — short, fact-seeking, adversarially written. V4-Pro-Max posts 57.9, a large margin over the prior open-model best.

AA-Omniscience

Measures factuality minus hallucination across economically relevant domains. Scored on a −100 to +100 scale: right answers +1, hallucinations −1, abstentions 0. A negative score means the model hallucinates more than it knows.

Agentic & Tool Use

τ²-Bench Telecom

Dual-control conversational benchmark. Both the agent and the simulated user can take actions — the pair must coordinate to resolve telecom support scenarios. Tests multi-turn state sharing, not one-shot tool calls.

GDPval-AA

OpenAI’s GDPval (220 real deliverables from 44 occupations across 9 GDP sectors, authored by industry pros with ~14 years of experience) wrapped in Artificial Analysis’s evaluation harness. Scoring is Elo via blind pairwise comparisons, anchored to GPT-5.1 Non-Reasoning at 1000.

IFBench

58 verifiable, out-of-domain instruction-following constraints. Does the model actually respect your output format requirements?

Long Context

AA-LCR (Long Context Reasoning)

100 hard questions spanning ~100k tokens across 7 document categories (company reports, legal, academia, government consultations, etc.). Requires a 128K context minimum. Answers are graded by a Qwen3 checker model for equality.

The ClawBench Family

Not in DeepSeek’s report, but part of the same agentic-evaluation wave — and worth knowing:

• ClawBench (clawbenchlab) — 30 business-workflow tasks across 5 scenarios (Office, Research, Content, Data, SWE), with intentionally embedded real-world traps like naming inconsistencies and missing directories
• MM-ClawBench — MiniMax’s OpenClaw-derived agentic benchmark
• LiveClawBench — Triple-Axis Complexity Framework (Environment / Cognitive / Runtime) with controlled pairs for attribution analysis
• WildClawBench (InternLM) — 60 hand-built tasks, ground truth injected only after agent completion to eliminate leakage

The Takeaway

The V4 scorecard confirms a pattern: for pure coding, open weights have caught up (LiveCodeBench 93.5, Codeforces 3206). For long-horizon agentic work (SWE-bench Pro, Terminal-Bench 2.0), closed frontier still leads. For frontier reasoning (HLE, GPQA Diamond), everyone is bunched up and benchmarks are actively being replaced faster than models can saturate them.

If you’re picking a benchmark to report on, the rule of thumb for 2026: prefer ones that (a) have a live contamination-resistance mechanism (dated problems, private splits, or copyleft-only sources), and (b) aren’t already within a point of the ceiling.