GPT-6 Astra wins on coding (100 vs 93) and writing quality. DeepSeek V4-Pro wins on price ($0.435 vs $10/1M input). For most workflows, GPT-6 Astra is the stronger default — openai's frontier answer to fable 5.1 — computer-use and agentic-coding leader at $10/$50.
Last verified Sep 4, 2026/Model data modified Sep 4, 2026
Rankings refresh dailyScored on 6 criteriaNo paid rankings
OpenAIPremium
Input cost
$10.00/1M
Context
1.1M tokens
Speed
Deliberate
Clear recommendation block
The shortest way to see the safest default, the lower-cost option, and the specialist pick before you read deeper.
DeepSeek V4-Pro is the better pick when response speed matters more than maximum reasoning depth.
DeepSeekBudget
Best for
Frontier-level coding and reasoning on a budget
Price
$0.43/1M
Context
1M tokens
Why this page recommends it
GPT-6 Astra leads on coding with a score of 100 vs 93 for DeepSeek V4-Pro.
GPT-6 Astra has the larger context window: 1.05M vs 1M for DeepSeek V4-Pro.
DeepSeek V4-Pro is cheaper at $0.435/1M input tokens vs $10/1M for GPT-6 Astra.
Decision notes
GPT-6 Astra is the safer default: it is built for computer and browser use, long-horizon agentic coding, and frontier math and science work, which covers most of what people bring to this comparison.
Switch to DeepSeek V4-Pro when your work is mostly frontier-level coding and reasoning on a budget; on that narrower brief it is the better tool.
DeepSeek V4-Pro is the more cost-efficient option at $0.435/1M input — GPT-6 Astra costs 23x more per input token, so the gap is worth taking seriously wherever token volume rather than peak quality drives the bill.
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#1GPT-6 Astra91 pts
#2DeepSeek V4-Pro83 pts
Quality first
GPT-6 Astra
OpenAI / Premium / Sep 4, 2026
91
OpenAI's frontier answer to Fable 5.1 — computer-use and agentic-coding leader at $10/$50.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
You are cost-sensitive or latency-bound — GPT-5.6 Sol is a fifth of the price — or you need it today in an Enterprise workspace, where it is off by default, or for offensive-security work, which it refuses outside OpenAI Daybreak.
Recommended comparisons
The fastest way to see where the recommendation shifts when your priority changes.
Capability scores are out of 100 and reflect our own weighting of published benchmarks and production signals — see how we evaluate models. “Est. month” assumes 10M input and 2M output tokens at list price, with no batch or caching discounts applied, so treat it as a ceiling.
The case for each model
What each one is genuinely good at, where it falls down, and the situations we would steer you away from it — not just the headline score.
OpenAI's September 3, 2026 frontier release — the first GPT-6 model and OpenAI's answer to Claude Fable 5.1 two days earlier. State of the art on computer use (OSWorld 2.0 72.6% in ~47% less time than GPT-5.6 Sol), agentic coding (Terminal-Bench 4.0 57.9%), and frontier math (FrontierMath Tier 4 97.6%). $10/$50 per 1M tokens, 1.05M context, 128K output, knowledge cutoff April 30, 2026.
Input
$10.00/1M
Output
$50.00/1M
Context
1.1M tokens
Speed
Deliberate
What people actually use it for
Computer-use agents that fill forms, update CRMs, run QA in a browser — 72.6% on OSWorld 2.0 at ~40 minutes per task vs Sol's 75
Agentic coding in Codex with cross-context notes — 57.9% Terminal-Bench 4.0, ahead of Claude Fable 5.1 (55.8%)
Frontier math and science — 97.6% FrontierMath Tier 4 and 64.6% Terminal-Bench Science, both well clear of every rival OpenAI tested
Where it wins
OSWorld 2.0 72.6% vs 65.7% for GPT-5.6 Sol, in roughly 47% less time per task — the new computer-use ceiling
Terminal-Bench 4.0 57.9% — ahead of Claude Fable 5.1 (55.8%), Opus 5 (52.3%) and GPT-5.6 Sol (37.3%)
FrontierMath Tier 4 (v2) 97.6% vs Fable 5.1's 87.8%; Terminal-Bench Science 64.6% vs 52.6%; GPQA Diamond 96.0%
1.05M context with 96.3% on OpenAI MRCR 8-needle at 512K–1M (Sol: 73.8%) and 128K max output
OpenAI's lowest misaligned-outcome rates to date: 2.4% on its computer-use safety benchmark vs 22.0% for Sol, 0% scope-creep on impossible cyber tasks vs 48%
Where it falls down
$10/$50 per 1M — five times GPT-5.6 Sol's $2/$10 and the same premium as Claude Fable 5.1; Fast mode doubles it again
Trails Claude Fable 5.1 on Humanity's Last Exam with tools (57.2% vs 65.0%) and on the Artificial Analysis Intelligence Index (61.2 vs 65.7)
OpenAI published no SWE-bench Verified or SWE-bench Pro figure at launch, so it does not appear on our SWE-bench leaderboard
Rolling out over days, not instantly: Enterprise access is off by default, advanced cyber tasks are refused outside OpenAI Daybreak, and the safety layer can pause or stop legitimate agent runs
OpenAI's own system card finds its written reasoning harder to monitor than Sol's
Skip it if
You are cost-sensitive or latency-bound — GPT-5.6 Sol is a fifth of the price — or you need it today in an Enterprise workspace, where it is off by default, or for offensive-security work, which it refuses outside OpenAI Daybreak.
Our verdict
The new computer-use and agentic-coding ceiling, and OpenAI's first model priced like a Mythos-class Claude. Astra beats Claude Fable 5.1 on Terminal-Bench 4.0 (57.9% vs 55.8%), Terminal-Bench Science (64.6% vs 52.6%) and FrontierMath Tier 4 (97.6% vs 87.8%), and it is the only model with a credible OSWorld 2.0 result above 70%. It loses to Fable 5.1 on Humanity's Last Exam and on Artificial Analysis's index, costs five times GPT-5.6 Sol, and ships without a SWE-bench number. If your work is computer use, browser agents or math, it is the pick; for everyday coding at scale, Sol at $2/$10 remains the value default.
Released September 3, 2026. API ID gpt-6-astra; rolling out over the coming days to ChatGPT Plus, Pro, Business and Enterprise (usage inside existing allowances; GPT-6 Astra Pro for Pro/Business/Enterprise; Enterprise off by default), the OpenAI API, Microsoft Azure and Amazon Bedrock. Standard API pricing $10/$50 per 1M tokens; Fast mode is up to 2x speed at 2x price; cache reads and writes have separate rates. Model docs list 1,050,000 context, 128,000 max output, knowledge cutoff April 30, 2026, reasoning efforts up to 'max'. Published launch numbers (Astra / GPT-5.6 Sol / Fable 5.1 / Opus 5): OSWorld 2.0 72.6 / 65.7 / — / 70.2; Terminal-Bench 4.0 57.9 / 37.3 / 55.8 / 52.3; Terminal-Bench Science 0.1 64.6 / 22.4 / 52.6 / 30.0; FrontierMath Tier 4 v2 97.6 / 83.0 / 87.8 / 73.2; GPQA Diamond 96.0 / 94.6 / 93.7 / 93.7; Humanity's Last Exam w/ tools 57.2 / — / 65.0 / 63.6; AutomationBench 41.4 / 18.1 / 31.4 / 26.9; DeepSWE v1.1 74.1 / 72.7 / 67.4 / 73.7; ARC-AGI-2 95.0 / 92.5 / 90.0 / 90.4; ARC-AGI-3 99.9 (OpenAI responses-API harness; ARC Prize's stateless runs score far lower) / 7.8 / — / 30.2; ExploitBench 100.0 / 78.5 / — / 70; SRE-Bench 88.0 / 55.9; Artificial Analysis Intelligence Index v4.1.1 61.2 / 60.9 / 65.7 / 63.1. Meets the Critical threshold for cybersecurity under OpenAI's Preparedness Framework; advanced cyber workflows gated behind OpenAI Daybreak. All figures from OpenAI's launch post and model docs, verified September 4, 2026.
DeepSeek's 1.6T-parameter (49B active) MoE flagship with hybrid sparse attention — near-frontier coding and reasoning at roughly a tenth of closed-rival pricing, MIT-licensed open weights.
Input
$0.43/1M
Output
$0.87/1M
Context
1M tokens
Speed
Balanced
What people actually use it for
Repository-level coding — 80.6% SWE-bench Verified (self-reported), the top open-weights score at release
Self-hosted frontier capability under an MIT license
Where it wins
80.6% SWE-bench Verified (self-reported) — reported as tied with Gemini 3.1 Pro
93.5% LiveCodeBench and Codeforces 3206 — elite competitive-coding results
1M context with 384K max output at $0.87/1M output — an order of magnitude cheaper than closed frontier models
Where it falls down
Independent harnesses report much lower agentic scores than the self-reported numbers; trails GPT-5.6 and Opus-class on hard agentic evals
Peak-hour surge pricing doubles rates, a price increase is announced, and it's text-only (no vision)
Skip it if
You need vision input, verified agentic performance, or predictable pricing (surge pricing and an announced increase loom).
Our verdict
The open-weights frontier flagship of 2026. Self-reported numbers flatter it and independent agentic scores land lower, but even discounted it's the most capability per dollar in the directory's upper tier — with MIT-licensed weights.
Open-weight preview April 24; GA ~July 20, 2026. Off-peak pricing verified on api-docs.deepseek.com; Beijing-business-hours surge doubles it. Legacy deepseek-chat/reasoner endpoints retired July 24, 2026.
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FAQ
Is GPT-6 Astra better than DeepSeek V4-Pro?
GPT-6 Astra wins on more of the categories we score — coding, research, long context — so it is the better default of the two. DeepSeek V4-Pro is the better pick when your work is mostly frontier-level coding and reasoning on a budget. Neither is universally "better": GPT-6 Astra is aimed at computer and browser use and long-horizon agentic coding, DeepSeek V4-Pro at frontier-level coding and reasoning on a budget.
Which is cheaper — GPT-6 Astra or DeepSeek V4-Pro?
DeepSeek V4-Pro is cheaper at $0.435/1M input and $0.87/1M output. GPT-6 Astra costs $10/1M input and $50/1M output.
Which has a larger context window — GPT-6 Astra or DeepSeek V4-Pro?
GPT-6 Astra has the larger context window at 1.05M tokens vs DeepSeek V4-Pro's 1M. For large document analysis, GPT-6 Astra is the stronger pick.
Is GPT-6 Astra or DeepSeek V4-Pro better for coding?
GPT-6 Astra is better for coding with a score of 100 vs DeepSeek V4-Pro's 93 (out of 100). GPT-6 Astra is the overall coding leader in this directory at 100/100.
Which is faster — GPT-6 Astra or DeepSeek V4-Pro?
DeepSeek V4-Pro is faster with a balanced speed rating (score: 3) vs GPT-6 Astra's deliberate rating (score: 2). Speed matters most for interactive and high-throughput work; for batch jobs the GPT-6 Astra latency penalty is usually invisible.
What are the downsides of GPT-6 Astra?
$10/$50 per 1M — five times GPT-5.6 Sol's $2/$10 and the same premium as Claude Fable 5.1; Fast mode doubles it again. Trails Claude Fable 5.1 on Humanity's Last Exam with tools (57.2% vs 65.0%) and on the Artificial Analysis Intelligence Index (61.2 vs 65.7). OpenAI published no SWE-bench Verified or SWE-bench Pro figure at launch, so it does not appear on our SWE-bench leaderboard. Avoid it if you are cost-sensitive or latency-bound — GPT-5.6 Sol is a fifth of the price — or you need it today in an Enterprise workspace, where it is off by default, or for offensive-security work, which it refuses outside OpenAI Daybreak. That is the main case for looking at DeepSeek V4-Pro instead.
What are the downsides of DeepSeek V4-Pro?
Independent harnesses report much lower agentic scores than the self-reported numbers; trails GPT-5.6 and Opus-class on hard agentic evals. Peak-hour surge pricing doubles rates, a price increase is announced, and it's text-only (no vision). Avoid it if you need vision input, verified agentic performance, or predictable pricing (surge pricing and an announced increase loom). Against GPT-6 Astra specifically, the gap shows up most on coding (100 vs 93).
What does a month of real work cost on GPT-6 Astra vs DeepSeek V4-Pro?
Take a moderate workload of 10M input and 2M output tokens a month. GPT-6 Astra runs $200.00 (at $10/1M in and $50/1M out); DeepSeek V4-Pro runs $6.09 (at $0.435/1M in and $0.87/1M out). That is a $193.91/month difference — DeepSeek V4-Pro is the cheaper of the two at this volume, and the gap scales linearly as you send more. Output tokens dominate the bill on both, so prompt length matters far less than response length.
Can I use GPT-6 Astra and DeepSeek V4-Pro together?
Yes, and for most teams that beats picking one. A common split is GPT-6 Astra for computer and browser use and long-horizon agentic coding, with DeepSeek V4-Pro handling frontier-level coding and reasoning on a budget. Routing high-volume, low-stakes calls to DeepSeek V4-Pro at $0.435/1M and reserving GPT-6 Astra for the hard cases is usually the cheapest arrangement that does not cost you quality.