Model comparison
DeepSeek V4 Pro vs GPT-6 Astra
DeepSeek against OpenAI, compared on context, price, and attributed benchmark results.
Recorded profile verification October 1, 2026Individual provider fields may changeEvidence confidence: Medium — see receipts below
DeepSeek
DeepSeek V4 Pro
Frontier · Open weights
OpenAI
GPT-6 Astra
Frontier
| Specification | DeepSeek V4 Pro | GPT-6 Astra |
|---|---|---|
| Provider | ||
| Provider | DeepSeek | OpenAI |
| Tier | ||
| Tier | Frontier | Frontier |
| Context window | ||
| Context window | 1M | Winner: 1.05M |
| Max output | ||
| Max output | Winner: 384K | 128K |
| Input / 1M tokens | ||
| Input / 1M tokens | Winner: $1.32 | $10 |
| Output / 1M tokens | ||
| Output / 1M tokens | Winner: $3.96 | $50 |
| Weights | ||
| Weights | Open | Closed |
| Parameters | ||
| Parameters | 1.6T total / 49B active (MoE) | Not verifiedUnverified |
| Reasoning levels | ||
| Reasoning levels | low, high, max | low, medium, high, xhigh, max |
| Modalities | ||
| Modalities | text | text, image |
| License | ||
| License | MIT | Not verifiedUnverified |
| API model id | ||
| API model id | deepseek-v4-pro | gpt-6-astra |
| Released | ||
| Released | April 24, 2026 | September 3, 2026 |
| Artificial Analysis Intelligence Index [max] · source date DeepSeek V4 Pro: 2026-10-01 | ||
| Artificial Analysis Intelligence Index [max] · source date DeepSeek V4 Pro: 2026-10-01 | 36 | Not verifiedUnverified |
| Artificial Analysis Intelligence Index [max] · source date GPT-6 Astra: 2026-10-01 | ||
| Artificial Analysis Intelligence Index [max] · source date GPT-6 Astra: 2026-10-01 | Not verifiedUnverified | 53 |
| SWE-bench Verified [max] · source date DeepSeek V4 Pro: 2026-04-24 | ||
| SWE-bench Verified [max] · source date DeepSeek V4 Pro: 2026-04-24 | 80.6 | Not verifiedUnverified |
| GPQA Diamond [max] · source date DeepSeek V4 Pro: 2026-04-24 | ||
| GPQA Diamond [max] · source date DeepSeek V4 Pro: 2026-04-24 | 90.1 | Not verifiedUnverified |
| GPQA Diamond · source date GPT-6 Astra: 2026-09-03 | ||
| GPQA Diamond · source date GPT-6 Astra: 2026-09-03 | Not verifiedUnverified | 96 |
| Humanity's Last Exam [max] · source date DeepSeek V4 Pro: 2026-04-24 | ||
| Humanity's Last Exam [max] · source date DeepSeek V4 Pro: 2026-04-24 | 48.2 | Not verifiedUnverified |
| Humanity's Last Exam · source date DeepSeek V4 Pro: 2026-08-13 | ||
| Humanity's Last Exam · source date DeepSeek V4 Pro: 2026-08-13 | 42.7 | Not verifiedUnverified |
| Humanity's Last Exam · source date DeepSeek V4 Pro: 2026-08-13 | ||
| Humanity's Last Exam · source date DeepSeek V4 Pro: 2026-08-13 | 60 | Not verifiedUnverified |
| Humanity's Last Exam · source date GPT-6 Astra: 2026-09-03 | ||
| Humanity's Last Exam · source date GPT-6 Astra: 2026-09-03 | Not verifiedUnverified | 57.2 |
| MMLU-Pro [max] · source date DeepSeek V4 Pro: 2026-04-24 | ||
| MMLU-Pro [max] · source date DeepSeek V4 Pro: 2026-04-24 | 87.5 | Not verifiedUnverified |
| Terminal-Bench 2.1 · source date DeepSeek V4 Pro: 2026-08-13 | ||
| Terminal-Bench 2.1 · source date DeepSeek V4 Pro: 2026-08-13 | 87.9 | Not verifiedUnverified |
| DeepSWE 1.1 · source date DeepSeek V4 Pro: 2026-08-13 | ||
| DeepSWE 1.1 · source date DeepSeek V4 Pro: 2026-08-13 | 62.7 | Not verifiedUnverified |
| DeepSWE 1.1 · source date GPT-6 Astra: 2026-09-03 | ||
| DeepSWE 1.1 · source date GPT-6 Astra: 2026-09-03 | Not verifiedUnverified | 74.1 |
| OSWorld 2.0 (offline) · source date GPT-6 Astra: 2026-09-03 | ||
| OSWorld 2.0 (offline) · source date GPT-6 Astra: 2026-09-03 | Not verifiedUnverified | 72.6 |
Dates beside benchmark scores refer to the linked source’s publication or capture date unless that source explicitly states when it measured the result. A shared URL alone does not establish comparable runs; unverified setups are marked. Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Benchmark receipts
Where each score comes from, and how far it can be compared across models.
- Source date2026-10-01Artificial Analysis (GPT-6 Astra (max))
Composite index blending reasoning, knowledge, and coding evals into one 0–100 score.
Comparable with caveatOnly same-version scores are comparable. v4.3.2 was rebaselined (the top score fell from 66 on v4.1.1 to ~58), so v4.1.1 figures must not be compared with v4.3.2 figures — check the cited source and its date.
- Source date2026-10-01Artificial Analysis (DeepSeek V4 Pro 0813 (max))
Composite index blending reasoning, knowledge, and coding evals into one 0–100 score.
Comparable with caveatOnly same-version scores are comparable. v4.3.2 was rebaselined (the top score fell from 66 on v4.1.1 to ~58), so v4.1.1 figures must not be compared with v4.3.2 figures — check the cited source and its date.
- Source date2026-09-03OpenAI GPT-6 Astra evaluation table (vendor-reported, with tools; maximum across evaluated efforts)
Frontier-knowledge ceiling: extremely hard multi-domain questions written to be near-impossible without deep expertise.
Comparable with caveatSubset/tool configurations (text-only vs with-tools) differ between vendors and materially change scores; must match configuration to compare.
- Source date2026-09-03OpenAI GPT-6 Astra evaluation table (vendor-reported; maximum across evaluated efforts)
Graduate-level science reasoning: multiple-choice questions written by domain PhDs.
Comparable with caveatDo not treat a fixed two-point band as universal measurement uncertainty; it depends on run count and evaluation setup. Chain-of-thought, prompt, and tool policy must match. The small pool limits fine-grained conclusions, and frontier scores can be near ceiling; Artificial Analysis dropped it from its Intelligence Index in v4.3.
- Source date2026-09-03OpenAI (vendor-reported) (v2026.08.08 offline set, partial reward; vendor research/API evaluation, maximum across evaluated efforts)
Computer-use: completing real OS tasks (files, apps, settings) in a sandboxed desktop.
Comparable with caveatOffline vs live interaction modes are NOT comparable; step budgets and screen resolution must match.
- Source date2026-09-03OpenAI (xhigh, mini-swe-agent; matches Datacurve's independent board (74.1% at $4.43/task, rank 1; board updated 2026-09-22))
Agentic software engineering: pass@1 on real repo tasks with reported cost per completed task.
Directly comparable
- Source date2026-08-13DeepSeek August 13 GA changelog (vendor) (0813 GA refresh; vendor report, exact run configuration unverified)
Agentic software engineering: pass@1 on real repo tasks with reported cost per completed task.
Directly comparable
- Source date2026-08-13DeepSeek August 13 GA changelog (vendor) (0813 GA refresh; vendor report, exact run configuration unverified)
Agentic terminal work: multi-step tasks executed in a sandboxed shell environment.
Comparable with caveatNot comparable with Terminal-Bench 3.0 or 4.0 (different task sets) or v1; harness configuration (container, time limits) also shifts results.
- Source date2026-08-13DeepSeek August 13 GA changelog (vendor) (0813 GA refresh; with tools; vendor report, exact run configuration unverified)
Frontier-knowledge ceiling: extremely hard multi-domain questions written to be near-impossible without deep expertise.
Comparable with caveatSubset/tool configurations (text-only vs with-tools) differ between vendors and materially change scores; must match configuration to compare.
- Source date2026-08-13DeepSeek August 13 GA changelog (vendor) (0813 GA refresh; without tools; vendor report, exact run configuration unverified)
Frontier-knowledge ceiling: extremely hard multi-domain questions written to be near-impossible without deep expertise.
Comparable with caveatSubset/tool configurations (text-only vs with-tools) differ between vendors and materially change scores; must match configuration to compare.
- Source date2026-04-24DeepSeek V4 Pro HF model card (vendor, Think Max, exact match) (Original April 24 checkpoint; historical vendor evaluation, not the served 0813 refresh)
Broad knowledge across 14 domains with harder, reasoning-focused multiple-choice questions.
Comparable with caveatFrontier models cluster in the high 80s–90s; near-ceiling differences are weak evidence. CoT vs direct answering must match.
- Source date2026-04-24DeepSeek V4 Pro HF model card (vendor, Think Max, with tools; 37.7 no tools) (Original April 24 checkpoint; historical vendor evaluation, not the served 0813 refresh)
Frontier-knowledge ceiling: extremely hard multi-domain questions written to be near-impossible without deep expertise.
Comparable with caveatSubset/tool configurations (text-only vs with-tools) differ between vendors and materially change scores; must match configuration to compare.
- Source date2026-04-24DeepSeek V4 Pro HF model card (vendor, Think Max, pass@1) (Original April 24 checkpoint; historical vendor evaluation, not the served 0813 refresh)
Graduate-level science reasoning: multiple-choice questions written by domain PhDs.
Comparable with caveatDo not treat a fixed two-point band as universal measurement uncertainty; it depends on run count and evaluation setup. Chain-of-thought, prompt, and tool policy must match. The small pool limits fine-grained conclusions, and frontier scores can be near ceiling; Artificial Analysis dropped it from its Intelligence Index in v4.3.
- Source date2026-04-24DeepSeek original V4 Pro HF card (vendor, Think Max; verifier/harness comparability unverified) (Original April 24 checkpoint; historical vendor evaluation, not the served 0813 refresh)
Real GitHub issue resolution: does the model's patch pass the hidden tests.
Comparable with caveatPost-audit vendor claims and pre-audit scores sit on different task trust levels; scaffolding (agent harness, compute budget) also dominates results. Never aggregate across scaffolds.
Pricing tiers
DeepSeek V4 Pro: Official peak rates $1.32/$3.96 per MTok; off-peak $0.66/$1.98 (cache-miss). Cache hit $0.044 peak / $0.022 off-peak. Peak hours are weekdays 01:00–04:00 and 06:00–10:00 UTC, excluding Chinese public holidays; weekends, holidays and all other hours are off-peak. Open weights (MIT). 1.6T/49B active MoE.
GPT-6 Astra: Standard $10/$50 per MTok (≤272K input); above 272K the full request bills $20/$75. Cached input $1/$2 and cache writes $12.50/$25 for short/long context. Batch/Flex are half Standard; Fast is 2x. Ultrafast is 6x: $60/$300 short, $120/$450 long, through Responses with global/US processing; no EU or other regional inference residency. Regional/FedRAMP processing adds 10% where eligible. Knowledge cutoff Apr 30 2026. Enterprise model access is disabled by default.
DeepSeek V4 Pro
DeepSeek V4 Pro is DeepSeek's large open-weight (MIT) MoE with a 1M-token context — DeepSeek says the cheaper V4.1-Flash now beats it.
Best for
- Cost-sensitive hosted agents
- Open-weight deployments
- High-volume coding
Watch out
The current tariff identifies DeepSeek-V4-Pro-0813. DeepSeek's current changelog reverses the earlier September 14 re-route plan and says it will continue serving V4 Pro at unchanged rates until further notice. Original April benchmark/checkpoint results are not the current 0813 evaluation. Self-hosting memory depends on quantization, offloading and hardware.
GPT-6 Astra
OpenAI describes GPT-6 Astra as its most capable model for complex reasoning, coding and computer use, and the first it gates at the Preparedness Framework's 'Critical' cybersecurity threshold.
Best for
- Computer-use agents
- Agentic coding
- Hard reasoning
Watch out
OpenAI places Astra at its Critical cybersecurity threshold and disables it by default for Enterprise. Tool calling requires Responses; none reasoning and custom temperature/top_p/logprobs are unsupported. Long prompts above 272K increase the full-request rate. GPT-6.1 Sol has lower base token prices; compare results on your own workload.
Benchmark
DeepSWE 1.1 in context
Only GPT-6 Astra has a published DeepSWE 1.1 result. It is shown against the wider field, with cost per completed task alongside the score.
Local leader
GPT-6 Astra [xhigh]
74.1%
Rows shown
28
Best source-matched Pass@1 per model
Snapshot date
2026-10-01
Mirrored from deepswe.datacurve.ai
Better is toward the top-right (higher pass rate, lower cost). X-axis is reversed to match DeepSWE’s public chart. v1.1 uses average cost, output tokens and steps. Legacy v1 transcriptions are excluded here until an exact primary receipt is retained.
| # | Model | Pass@1 | Cost / task | Tokens / task | Steps / task |
|---|---|---|---|---|---|
| 1 | GPT-6 Astra [xhigh] | 74.1% | $4.43 | 30k | 29 |
| 2 | Gemini 3.8 Flash [high] | 73.8% | $2.36 | 143k | 166 |
| 3 | Claude Opus 5 [max] | 73.6% | $11.84 | 118k | 99 |
| 4 | GPT-5.6 Sol [max] | 72.7% | $8.39 | 60k | 61 |
| 5 | Claude Fable 5 [xhigh] | 69.9% | $13.41 | 80k | 68 |
| 6 | GPT-5.6 Terra [max] | 69.6% | $4.95 | 72k | 76 |
| 7 | GLM 5.3 [max] | 69.0% | $3.99 | 80k | 124 |
| 8 | Kimi K3 [max] | 68.5% | $4.65 | 82k | 98 |
| 9 | Grok 4.6 [medium] | 67.5% | $3.45 | 50k | 70 |
| 10 | GPT-5.6 Luna [max] | 67.2% | $3.03 | 73k | 102 |
| 11 | GPT-5.5 [xhigh] | 67.0% | $7.23 | 46k | 82 |
| 12 | Gemini 3.7 Flash [medium] | 65.5% | $2.03 | 94k | 117 |
| 13 | GLM 5.3 Flash [max] | 63.4% | $0.48 | 73k | 123 |
| 14 | DeepSeek V4-Pro [max] | 62.8% | $0.24 | 106k | 155 |
| 15 | Claude Opus 4.8 [max] | 59.0% | $13.22 | 135k | 120 |
| 16 | Qwen3.8-Max [xhigh] | 57.5% | $3.73 | 95k | 111 |
| 17 | Muse Spark 1.2 [xhigh] | 54.9% | $3.70 | 99k | 101 |
| 18 | Claude Sonnet 5 [max] | 53.8% | $26.40 | 214k | 268 |
| 19 | Grok 4.5 [high] | 53.8% | $2.42 | 36k | 61 |
| 20 | DeepSeek V4-Flash [max] | 53.3% | $0.10 | 108k | 153 |
| 21 | Muse Spark 1.1 [xhigh] | 53.3% | $2.36 | 74k | 96 |
| 22 | GPT-5.4 [xhigh] | 51.8% | $5.65 | 71k | 70 |
| 23 | Gemini 3.6 Flash [high] | 46.7% | $4.42 | 96k | 117 |
| 24 | GLM 5.2 [max] | 43.8% | $3.92 | 78k | 129 |
| 25 | Gemini 3.5 Flash [high] | 36.1% | $3.45 | 76k | 105 |
| 26 | Kimi K2.7 Code | 30.5% | $2.82 | 59k | 149 |
| 27 | Claude Sonnet 4.6 [high] | 29.9% | $5.52 | 76k | 134 |
| 28 | Gemini 3.1 Pro [high] | 11.7% | $2.14 | 28k | 76 |
“Best per model” shows each model’s highest source-matched Pass@1 in this capture; switch to all effort levels to see every retained configuration. Efforts differ across models, so this view is not a comparison at matched effort. The source was generated September 22 and captured October 1. Small gaps may not be statistically meaningful — confirm on deepswe.datacurve.ai.
When the cheaper one wins
DeepSeek V4 Pro is cheaper on output at $3.96 per million tokens against $50 for GPT-6 Astra — about 13×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. On DeepSWE 1.1, DeepSeek V4 Pro is 62.8% Pass@1 at $0.24/task versus GPT-6 Astra at 74.1% / $4.43/task. This compares standard short-context token rates, not total cost per accepted task. Include input, cache, context tiers, schedules, retries and review in your pilot.
Run the model pickerEvidence confidence: Medium
How strong and complete the evidence behind this comparison is — not a prediction of which model is better.
- Pricing verified on both sides — Input and output rates are verified for both models.
- 4/5 core specs verified on both sides — Not published for at least one side: parameter count.
- No directly aligned benchmark setup — No benchmark has matching cited sources, source dates, units, effort and recorded run details on both models.
- Verified within the last 90 days — Older of the two catalog checks was 25 days ago.
- Both models carry source citations — Each side has at least two catalog sources on record.
Source receipts
Each catalog figure was checked against the provider or an independent second source on the date shown.
- DeepSeek V4 Pro: Whole-profile primary source check October 1 (accessed 2026-10-01)
- DeepSeek V4 Pro: Whole-profile primary source check October 1 (accessed 2026-10-01)
- DeepSeek V4 Pro: DeepSeek current API version, holiday tariff and 0813 GA benchmark publication (accessed 2026-10-01)
- DeepSeek V4 Pro: DeepSeek current tariff and served endpoint versions (accessed 2026-10-01)
- DeepSeek V4 Pro: DeepSeek — V4 news (accessed 2026-08-29)
- DeepSeek V4 Pro: DeepSeek API pricing (accessed 2026-08-29)
- DeepSeek V4 Pro: DeepSeek — V4.1-Flash release (V4-Pro phase-out) (accessed 2026-09-26)
- DeepSeek V4 Pro: DeepSeek API changelog (accessed 2026-09-26)
- GPT-6 Astra: OpenAI model card — whole-profile source check October 1 (accessed 2026-10-01)
- GPT-6 Astra: OpenAI current API pricing and processing tiers (accessed 2026-10-01)
- GPT-6 Astra: OpenAI API changelog — September 3 release; checked October 1 (accessed 2026-10-01)
- GPT-6 Astra: OpenAI — Introducing GPT-6 Astra (accessed 2026-09-03)
- GPT-6 Astra: OpenAI API pricing (gpt-6-astra $10/$50) (accessed 2026-09-03)
- GPT-6 Astra: CNBC — OpenAI launches GPT-6 Astra (accessed 2026-09-03)
- GPT-6 Astra: OpenAI model docs — gpt-6-astra (128K max output, effort levels) (accessed 2026-09-05)
- GPT-6 Astra: OpenAI API pricing (gpt-6-astra ≤272K $10/$50, >272K $20/$75) (accessed 2026-09-26)
Related comparisons
- Claude Fable 5.1 vs DeepSeek V4 Pro
- Claude Fable 5.1 vs GPT-6 Astra
- Claude Fable 5 vs DeepSeek V4 Pro
- Claude Fable 5 vs GPT-6 Astra
- Claude Mythos 5.1 vs DeepSeek V4 Pro
- Claude Mythos 5.1 vs GPT-6 Astra
Diving deeper on one model? DeepSeek V4 Pro · GPT-6 Astra
Common questions
DeepSeek V4 Pro vs GPT-6 Astra
Answered from the verified figures on this page rather than general guidance.
Is DeepSeek V4 Pro or GPT-6 Astra cheaper for input?
Is DeepSeek V4 Pro or GPT-6 Astra cheaper for output?
Which has the larger context window, DeepSeek V4 Pro or GPT-6 Astra?
Do DeepSeek V4 Pro and GPT-6 Astra support the same reasoning levels?
Should I use DeepSeek V4 Pro or GPT-6 Astra?
Can I self-host DeepSeek V4 Pro or GPT-6 Astra?
Next step
Choosing between them
The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.
- Input price: DeepSeek V4 Pro costs $1.32 per 1M tokens versus $10 for GPT-6 Astra — a 7.6x difference at the headline tier.
- Context: GPT-6 Astra takes 1.05M against 1M for DeepSeek V4 Pro — only decisive if your prompts approach the smaller window.
- Deployment: DeepSeek V4 Pro publishes weights you can self-host; the other is API-only.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Windsurf, or Lovable? Compare agentic harnesses · Latest releases.