DeepSeek V4 Pro vs GPT-5.5: Benchmarks & Cost Compared

GPT-5.5 is OpenAI's flagship closed model; DeepSeek V4 Pro 0813 is DeepSeek's open-source (MIT) 1.6T-parameter MoE that went GA on August 13, 2026^[2]. Both target the same buyer: teams who need frontier reasoning, long context, and strong agentic coding without locking into a single vendor.

The headline difference is transparency and price. V4 Pro ships with public weights on Hugging Face^[5], can be self-hosted, and costs a small fraction of GPT-5.5 per token. The trade-off is a real, measurable gap on aggregate benchmarks — how large depends on who measures.

• V4 Pro 0813: MIT-licensed weights, 1M context, 49B active params.
• GPT-5.5: closed API, benchmark-leading aggregate scores (BenchLM #11 vs V4 Pro #53)^[8].
• Both support reasoning effort controls and OpenAI-compatible tool calling.

BenchLM's shared-eval composite (11 benchmarks, updated August 15, 2026) puts V4 Pro 0813 at 61.2/100 (rank #53) versus GPT-5.5 at 73.4/100 (rank #11)^[8]. The gap is real but concentrated in specific areas rather than uniform.

On knowledge, the models are within a few points. The composite gap comes mostly from long-horizon agentic and multimodal-style tasks where OpenAI's closed pipeline still holds a lead^[8]. For text reasoning, math, and code — the workloads most teams actually run — the delta is thin.

On patch-style software engineering with a neutral third-party harness, V4 Pro 0813 hits 96.40% on SWE-bench Verified — the #2 result in the Codersera/Vals AI leaderboard, ahead of GPT-5.6 Sol (96.20%) and just behind Claude Opus 5 (97.00%)^[7].

GPT-5.5 itself is not in that leaderboard row, but GPT-5.6 Sol — its successor — sits below V4 Pro on this benchmark. The pattern holds across coding evals: V4 Pro is a top-3 open model on repository-level fixes, while end-to-end agentic runs (Terminal-Bench with reference harnesses) still favor the closed incumbents^[4].

One nuance worth knowing before you switch: the SWE-bench gap between the two is inside the noise band on small test sets, but V4 Pro's advantage on cost is not. Independent evals also consistently show GPT-5.5 winning on ambiguous, open-ended instructions and on long browser-automation tasks — precisely the workloads where a closed product team has invested in RL for years. Plan your evaluation around your own task mix rather than the aggregate score.

The cost difference is where V4 Pro changes the economics. At $0.022 per SWE-bench test, V4 Pro is about 1/59th of Claude Opus 5's cost and 1/36th of Grok 4.6's^[7]. Against GPT-5.5-class API pricing — typically $0.50-$2.00 per 1M tokens at flagship tier — V4 Pro's $0.435/$0.87 base is already a fraction, and the gap widens further with context caching and off-peak scheduling^[1][3].

For a workload doing 10,000 SWE-bench-style test executions, V4 Pro would cost about $220 at the $0.022 figure — versus thousands of dollars on the closed flagship. That math is why teams run quality gates on V4 Pro and reserve the closed model for final review.

# per 1M tokens (official, pre-8/16 flat rate)
#          input(miss)   input(hit)   output
# V4 Pro     $0.435       $0.003625    $0.87
# GPT-5.5    (flagship tier, typically 2-4x Pro's price)
#
# After 8/16 16:00 UTC: Pro off-peak $0.66/$1.98,
# peak $1.32/$3.96 — still far below GPT-5.5-class pricing.

Switch to V4 Pro if you value open weights, MIT licensing, and cost per token — and your workload is text reasoning, patch-style coding, or high-volume pipelines. Stay on GPT-5.5 (or keep it as a fallback) for long-horizon agentic autonomy, multimodal inputs, and absolute aggregate benchmark leadership^[8].

The pragmatic answer is often both: route routine work to V4 Flash or V4 Pro, and escalate to a closed flagship when an agent needs maximum autonomy. The pricing change on August 16 makes that hybrid math even more favorable to DeepSeek on the base layer^[3].

• Open-source teams needing self-hosting → V4 Pro (MIT weights on Hugging Face)^[5].
• High-volume code review / PR triage → V4 Pro; $0.022 per test at near-frontier accuracy^[7].
• Autonomous multi-hour agents with browsing → GPT-5.5; BrowseComp-style gaps remain^[8].
• Compliance-sensitive or offline deployments → V4 Pro self-hosted via vLLM.