DeepSeek V4 Pro 0813 GA: Open-Weight Frontier Beats Opus 4.8 on Terminal Bench

TL;DR

DeepSeek-V4-Pro-0813 went generally available on August 13, 2026, graduating from its April preview to a production build across app, web, and API. The 1.6-trillion-parameter Mixture-of-Experts model — with 49B active parameters per token — posts a Terminal-Bench 2.1 score of 87.9%, edging past Claude Opus 4.8's 85.0% and marking the first time an open-weight model has overtaken a frontier closed model on that benchmark. Independently verified SWE-bench Verified results hit 96.40%, making it the highest-scoring open-weight model on the board. But the GA weights aren't on Hugging Face yet, a significant price hike hits August 16, and several self-reported benchmarks still await independent replication.

The GA Release: What Actually Changed

DeepSeek staged the V4 rollout deliberately. The family first appeared as open-weight previews on April 24, 2026, with both Pro and Flash variants released under the MIT license. On July 31, 2026, the smaller Flash model (284B total / 13B active) graduated first with open weights on Hugging Face. The Pro build promised to "follow soon" — and that promise materialized as the 0813 build, which appeared on the API endpoint on August 12 and was officially confirmed GA on August 13.

The GA release adds four concrete things over the April preview:

Three reasoning effort levels: low, high, and max — letting developers trade latency for accuracy
Native Responses API support for agentic tool-use workflows
Max output tokens extended to 384K (up from the preview's limit)
DeepSeek Harness (dsh) — a new native coding agent harness, which racked up 72K GitHub stars overnight

What GA did not include: updated open weights for the 0813 checkpoint. The Hugging Face repository still hosts the April preview weights. The production build is currently API-and-app-only.

Architecture: MoE at Unprecedented Scale

DeepSeek's bet on Mixture-of-Experts (MoE) reaches its most extreme implementation in V4 Pro. The model packs 1.6 trillion total parameters but only activates about 49 billion per token — a 3% activation ratio that keeps inference costs from spiraling while giving each token access to specialized expert networks.

Key architectural innovations include:

Hybrid attention system: Combines Compressed Sparse Attention with Heavily Compressed Attention for efficient long-context processing
Manifold-Constrained Hyper-Connections — a novel routing mechanism for expert selection
Muon optimizer — used during training for improved convergence
Efficiency gains: At 1M-token context, V4 Pro requires only 27% of the single-token inference FLOPs and 10% of the KV-cache memory of DeepSeek V3.2

The architecture is shared with V4 Flash but scaled up dramatically. Both models support a 1M-token context window — roughly 1,500 A4 pages of text.

Benchmark Results: Where V4 Pro Wins

The 0813 build shows dramatic gains over the April preview, particularly in agentic capabilities. Here's the full picture:

Self-Reported Benchmarks (DeepSeek's evaluation)

Benchmark	April Preview	V4 Pro 0813	Delta
Terminal-Bench 2.1	72.1%	87.9%	+15.8
DeepSWE	12.8%	62.7%	+49.9
NL2Repo	38.5%	61.5%	+23.0
CyberGym	52.7%	83.3%	+30.6
Toolathlon Verified	55.9%	74.1%	+18.2
AutomationBench	12.8%	31.8%	+19.0
DSBench-FullStack	—	71.1%	—
DSBench-Hard	—	67.2%	—

Independently Verified Results

Vals.ai ran V4 Pro 0813 through their independent evaluation pipeline and confirmed:

SWE-bench Verified: 96.40% — ranked #2 of 82 models, highest-scoring open-weight model on the board (ahead of Kimi K3 at 93.40%)
Vals Index: 52.37% — ranked #18 of 46 models, up 9.48 points from DeepSeek V4 (42.89%)
Cost per test: $0.02 — versus $1.29 for Claude Opus 5 at 97.00%

Artificial Analysis scored V4 Pro 0813 at 53 on their Intelligence Index, making it the second most intelligent open-weight model they've benchmarked. It's also more token-efficient than the April version, using ~30% fewer output tokens on the index.

The Terminal-Bench Headline

This is where the story gets interesting. On Terminal-Bench 2.1 — the benchmark that tests real-world terminal/computer-use tasks — V4 Pro 0813 scores 87.9%, compared to:

Claude Opus 4.8: 85.0%
Claude Fable 5: 88.0%
V4 Flash 0731: 82.7%
GLM-5.2: 81.0%

V4 Pro doesn't just beat Opus 4.8 — it nearly ties Claude Fable 5, Anthropic's newest model. For an open-weight model at a fraction of the cost, that's a significant milestone.

V4 Pro vs Claude Opus 4.8: The Full Comparison

The headline says "beats Opus 4.8 on Terminal Bench," but the full picture is more nuanced. Neither model is uniformly better — they split wins across different capabilities.

Dimension	DeepSeek V4 Pro 0813	Claude Opus 4.8
Terminal-Bench 2.1	87.9%	85.0%
LiveCodeBench	93.5%	88.8%
SWE-bench Verified	96.40%	97.00%
SWE-bench Pro	55.4%	69.2%
AA Intelligence Index	52-53	57-61
Cost (per M output tokens)	$0.87	$25.00
Speed (tokens/sec)	72.4	55.7
Time to first token	1.77s	30.22s
Context window	1M	1M
Max output tokens	384K	—
Open weights	MIT (preview only)	Closed

The pattern is clear: DeepSeek wins on cost, speed, algorithmic coding, and terminal-use tasks. Opus 4.8 wins on repo-scale software engineering, general intelligence, and reliability. The 28× price gap makes V4 Pro the default choice for batch processing, high-volume coding loops, and cost-sensitive agentic workflows.

The Open-Weight Question

Here's where the story gets complicated. DeepSeek built its reputation on open-weight releases, and the V4 family was announced with MIT-licensed weights. But there's a catch:

V4 Flash 0731: Weights are live on Hugging Face under MIT. Self-hostable today.
V4 Pro 0813 (GA): Weights are NOT released. The Hugging Face repository still shows the April preview checkpoint.

This means teams relying on self-hosting for data governance are currently running the April preview, not the production build. DeepSeek has not explicitly stated when (or if) the 0813 weights will be published. The decision signals a potential strategic pivot toward API monetization for the flagship model.

For regulated industries, this matters. The hosted API transits data to DeepSeek's servers in China, subjecting it to Chinese data laws. Self-hosting the MIT-licensed weights on non-Chinese infrastructure eliminates the data-transit concern — but only if those weights actually correspond to the production checkpoint.

Pricing: The Good and the Bad

Current Pricing (through August 15, 2026)

DeepSeek is offering V4 Pro 0813 at the same pricing as the earlier preview model:

Input: $0.435 per million tokens
Output: $0.87 per million tokens
Cache hits: ~$0.004 per million tokens (99% discount)

Peak Pricing (starting August 16, 2026)

A significant restructure takes effect on August 16:

Input: $1.32 per million tokens (+204%)
Output: $3.96 per million tokens (+355%)
Cache hits: $0.044 per million tokens (97% discount, but 12× higher than before)
Off-peak: 50% discount on all tiers

The blended price increase is approximately 264%. Even at peak pricing, V4 Pro remains dramatically cheaper than Claude Opus 4.8 ($3.85 per M tokens at a 7:2:1 cache ratio). But the window of "absurdly cheap frontier intelligence" is closing.

The 24-Hour Showdown: V4 Pro vs GLM-5.3

DeepSeek wasn't the only Chinese lab shipping frontier models this week. Zhipu released GLM-5.3 on August 14, 2026 — exactly 24 hours after V4 Pro's GA — with a completely different philosophy:

Dimension	V4 Pro 0813	GLM-5.3
Architecture	New 1.6T MoE, 49B active	Same 743B base as GLM-5.2
Weights	MIT (preview only)	Open in ~2 weeks after security review
SWE-bench Verified	96.40%	Not yet reported
CyberGym	83.3%	84.5%
AutomationBench	31.8%	48.2%
Max output	384K	128K
API pricing	$0.435/$0.87 (rising to $1.32/$3.96)	Subscription $18-168/mo, API TBD

Both claim the open-weights coding crown. V4 Pro owns SWE-bench Verified and raw scale. GLM-5.3 edges agentic benchmarks and keeps the same architecture with extended post-training. The competition is pushing both labs to ship faster and cheaper.

What This Means for Developers

When to pick V4 Pro 0813

Algorithmic and competitive programming — LiveCodeBench at 93.5% beats Opus 4.8
Terminal/computer-use agents — Terminal-Bench 2.1 at 87.9% leads the open-weight field
High-volume batch processing — at 28× cheaper than Opus, the economics speak for themselves
Long-context reasoning — 1M context with 384K output is ideal for document analysis pipelines
Cost-sensitive agentic loops — cache-hit pricing near zero makes multi-turn agents affordable

When to stick with Opus 4.8

Repo-scale refactoring — SWE-bench Pro gap is 13.8 points (69.2% vs 55.4%)
Multi-file agentic work — the gap widens further on tasks touching 4+ files
Enterprise SLAs and support — Anthropic's reliability guarantees remain unmatched
Data governance — if you can't use a China-hosted API and the open weights aren't current

When to consider alternatives

Self-hosting: V4 Flash 0731 is the only current DeepSeek model with live GA weights
Multilingual beyond Mandarin/English: V4 Pro's AA Index scores reflect below-average multilingual coverage
Cybersecurity tasks: NIST's evaluation of the preview build showed a 32% score vs GPT-5.5's 71% on CTF-Archive-Diamond

The Verification Gap

DeepSeek's own evaluation table posts impressive numbers, but several caveats remain:

Baseline is self-referential: Most gains are measured against DeepSeek's own April preview, not against competitors
Independent audits are partial: Vals.ai confirmed SWE-bench Verified, but other benchmarks remain vendor-reported — a recurring concern in AI model evaluations
NIST evaluation covers the preview build: Whether 0813 has closed the cybersecurity and abstract reasoning gaps is unknown
No security audit of weights: No named, independent security audit of V4 Pro has been published

The open-weight community is already stress-testing these claims. The Reddit r/LocalLLaMA thread shows mixed reception — excitement about the benchmark jumps, skepticism about the lack of GA weights, and debate about whether the self-reported numbers will hold up under community replication.

FAQ

Is DeepSeek V4 Pro 0813 actually open source?

DeepSeek V4 Pro was announced with MIT-licensed weights, and the April preview checkpoint is available on Hugging Face. However, the 0813 GA build weights have not been released — only the April preview weights are public. This means you can self-host the preview model today, but you cannot self-host the production build that achieved the benchmark scores making headlines. DeepSeek has not stated when or if the 0813 weights will be published, marking a potential shift from their traditionally open approach toward API-first monetization for the flagship model.

How does V4 Pro 0813 compare to Claude Opus 4.8 on coding tasks?

The comparison depends heavily on the type of coding task. On SWE-bench Verified (single-bug fixes), V4 Pro scores 96.40% independently verified, nearly matching Opus 4.8's 97%. On SWE-bench Pro (repo-scale multi-file engineering), Opus 4.8 leads by 13.8 points at 69.2% vs 55.4%. On algorithmic coding (LiveCodeBench), V4 Pro wins at 93.5% vs 88.8%. On Terminal-Bench 2.1 (real-world terminal tasks), V4 Pro edges ahead at 87.9% vs 85.0%. The practical guidance is to route algorithmic and single-file work to V4 Pro, and reserve Opus for complex multi-file refactoring.

What hardware do I need to self-host DeepSeek V4 Pro?

Self-hosting V4 Pro at full precision requires multi-GPU H100 or H200 class hardware due to the 1.6 trillion total parameter count. Even with the 49B active parameters per token, the full weight matrix must be loaded into memory. The smaller V4 Flash (284B total / 13B active) can run on a single 80GB GPU when quantized. Community quantizations from Unsloth offer options ranging from a 103GB 3-bit version to a 162GB lossless 8-bit GGUF. For teams without multi-GPU infrastructure, the hosted API at $0.435/$0.87 per million tokens (pre-August 16 pricing) remains the practical access path.

What is the Terminal-Bench 2.1 benchmark and why does it matter?

Terminal-Bench 2.1 evaluates models on real-world terminal and computer-use tasks — the kind of agentic work where a model must navigate a shell, run commands, interpret output, and chain multi-step actions to achieve a goal. It is developed by the same team behind FrontierBench, which ranks models on professional computer-work task completion. V4 Pro 0813 scoring 87.9% on Terminal-Bench is significant because it tests the model's ability to act as an autonomous agent rather than just answer questions. This is the capability most directly relevant to building production AI agents, and it's the benchmark where open-weight models have historically lagged furthest behind frontier closed models.

Should I be concerned about data governance when using DeepSeek's API?

When using DeepSeek's hosted API, prompts and conversation history transit to DeepSeek's servers in China and are subject to Chinese data laws, including the Data Security Law and Personal Information Protection Law. For regulated industries — healthcare, finance, government — this is a material consideration that should be evaluated against your organization's data governance framework. Self-hosting the MIT-licensed weights on non-Chinese infrastructure eliminates the data-transit concern, but two caveats remain: the self-hosted weights are currently the April preview (not the 0813 production build), and no independent security audit of V4 Pro's weights has been published. Organizations should treat the hosted API as outside their data governance framework until these conditions change.

Will the August 16 price increase make V4 Pro uncompetitive?

Even at the new peak pricing of $1.32/$3.96 per million tokens (a ~264% blended increase), V4 Pro remains dramatically cheaper than frontier alternatives. Claude Opus 4.8 costs approximately $3.85 per million tokens at a typical cache ratio, making V4 Pro roughly 3× cheaper even at peak pricing — down from the current 28× gap but still significant. The off-peak 50% discount further helps for batch workloads. The bigger concern is the cache-hit pricing change: from 99% discount to 97% discount means cache hits now cost 12× more, which impacts multi-turn agentic loops that rely heavily on context reuse. For high-volume workflows, the advice is to lock in current pricing evaluations before August 16 and structure workflows to maximize off-peak usage.

DeepSeek V4 Pro 0813 GA: Open-Weight Frontier Beats Opus 4.8 on Terminal Bench