AI Model Comparison · Verified June 2026

DeepSeek vs ChatGPT in 2026: V4 vs GPT-5.5 Head-to-Head

Updated

Verified June 2026 head-to-head. DeepSeek V4-Pro is 28–107× cheaper on API than GPT-5.5 with competitive coding benchmarks (80.6% SWE-bench Verified). ChatGPT wins multimodal (DeepSeek has none), agentic workflows, US enterprise procurement, and Western consumer reach. Geopolitical procurement nuance: US government device bans exist (Navy, NASA, multiple states) — but private and commercial use remains legal. Open-weight runs on Together AI, Fireworks, DeepInfra, and NVIDIA NIM in US datacenters mitigate data sovereignty. Political censorship persists across all hosts because it's baked into post-training weights. Below: pricing, verified benchmarks, 8-use-case decision matrix, the procurement question, the censorship reality, and the honest SEO/GEO angle.

~34.5×

V4-Pro cheaper than GPT-5.5 (API output)

80.6%

DeepSeek V4-Pro SWE-bench Verified

$7.4B

DeepSeek funding round closed June 2026

MIT

V4-Pro + V4-Flash open-weight license

Sources: DeepSeek API docs, Hugging Face, TechTimes, CNBC

Which models are we comparing? (verified June 2026)

DeepSeek (June 2026)

  • V4-Pro — released April 24, 2026. 1.6T total / 49B active Mixture-of-Experts. 1M token context. Hybrid thinking modes (Non-think / Think-High / Think-Max). MIT-licensed open weights on Hugging Face.
  • V4-Flash — 284B total / 13B active MoE. 1M context. MIT license. Top Hugging Face trending slot within a week of release.
  • R1 — still available via API. Prior-gen reasoning model from January 2025. R1's RL training methodology was the technical breakthrough behind the “DeepSeek moment.”
  • R2 — NOT released as of June 2026. CEO Liang Wenfeng reportedly dissatisfied with performance; analyst forecasts slipped to Q3–Q4 2026.
  • No shipping multimodal product — no DALL-E / Sora / Voice equivalents. Vision roadmap rumored but not shipped.

OpenAI ChatGPT (June 2026)

  • GPT-5.5 — default since April 23, 2026. 1M token API context. Function Calling v2.
  • GPT-5.5 Instant — new default May 5, 2026. Faster latency for conversational tasks.
  • GPT-5.5 Pro — extended context. Available on Pro and Business tiers.
  • Multimodal — DALL-E + GPT-5 native image generation, Sora 2 video inside ChatGPT, Advanced Voice Mode, Canvas, Memory, GPT Store.
  • Agent Mode (formerly Operator) — virtual machine, visual browser, terminal, Gmail/GitHub integration.

Pricing comparison (June 2026)

DeepSeek has no consumer paid tier — chat.deepseek.com is fully free. API pricing is where the cost story lives. Verified live URLs: deepseek.ai/pricing, chatgpt.com/pricing.

DeepSeek

chat.deepseek.com

Free

Web product; no paid consumer tier. 127M MAU as of March 2026, mostly China-market.

V4-Flash API

$0.14 in / $0.28 out per 1M tokens

284B total / 13B active MoE. 1M context. ~107× cheaper output than GPT-5.5.

V4-Pro API

$0.435 in / $0.87 out per 1M tokens

1.6T total / 49B active MoE. 1M context. ~34.5× cheaper output than GPT-5.5.

R1 API (reasoning)

$0.55 in / $2.19 out per 1M tokens

Prior-gen reasoning model. R2 NOT yet released as of June 2026.

Cache hit pricing

$0.0028 / M (V4-Flash)

~98% discount on cached input. Compounds savings on agentic workloads with repeated context.

Free signup credit

5M tokens

No card required.

OpenAI ChatGPT

Free

$0

GPT-5.5 access with tight limits; ads in US

Go

$8/mo

Launched Jan 2026; global ad-supported tier

Plus

$20/mo

Deep Research 10 runs/mo, Sora, Codex, Agent Mode

Pro (entry)

$100/mo

Launched Apr 9, 2026; same models as $200, reduced limits

Pro

$200/mo

20× Plus limits, 250 Deep Research/mo, GPT-5.5 Pro

Business

$25/seat monthly · $20 annual

SOC 2 Type II, SAML SSO; 2-user min

Enterprise

Custom

150-seat minimum; custom data residency

GPT-5.5 API

$5 in / $30 out per 1M tokens

GPT-5.5 Pro: $30 in / $180 out per 1M

Concrete cost example: 1M token input + 1M token output

DeepSeek V4-Flash: $0.42

DeepSeek V4-Pro: $1.30

GPT-5.5: $35.00

GPT-5.5 Pro: $210.00

For agentic workloads with repeated context, DeepSeek's cache hit pricing (~98% discount on cached input) compounds the gap further. At 100M tokens/month of mixed input/output, V4-Pro saves ~$3,370/month vs GPT-5.5; V4-Flash saves ~$3,458/month.

Benchmark head-to-head

Most V4 benchmark numbers are vendor-reported. SWE-bench Verified 80.6% is the most independently corroborated figure. Read the table with vendor-vs-independent flags in mind.

BenchmarkDeepSeekChatGPTWinner
SWE-bench Verified80.6% (V4-Pro, tied Gemini 3.1 Pro for top open-weight)verify exact; competitive bandCompetitive — both frontier-class
SWE-bench Pro~55.4% (third-party; vendor has no official entry)58.6%ChatGPT (Claude Opus 4.8 wins category at 69.2%)
MMLU-Pro87.5% (vendor-claimed)91.4% (GPT-5.2)ChatGPT (within ~4 pts)
GPQA Diamond90.1% (vendor-claimed)verify exactDeepSeek vendor-claimed lead (independent confirmation pending)
Codeforces rating3206 (V4-Pro vendor-claimed)3168 (GPT-5.4 vendor-claimed)DeepSeek vendor-claimed lead
HumanEval~96.4%~96%Saturated — both near ceiling

Reading the table honestly. Both are frontier-class. DeepSeek V4 is competitive on coding (SWE-bench Verified 80.6% is genuinely top-tier open-weight). ChatGPT wins SWE-bench Pro on real-world complex PRs (and Claude Opus 4.8 wins the SWE-bench Pro category outright). On general knowledge (MMLU), the gap is small. For most use cases, the model that matches your workflow ecosystem and cost ceiling matters more than 1–2 benchmark points.

The 8-use-case decision matrix

Honest June 2026 winner picks across 8 common use cases.

1

Multi-file code / PR resolutionChatGPT

GPT-5.5 leads SWE-bench Pro (58.6% vs DeepSeek's ~55.4% third-party). Agent Mode maturity. Codex tooling. Claude Opus 4.8 actually wins this category at 69.2% — see our /compare/claude-vs-chatgpt for the head-to-head if coding is your primary use case.

2

Math-heavy reasoningClose call

DeepSeek V4 thinking-max mode competitive on GPQA / AIME-style benchmarks (vendor-claimed). Independent verification weaker than ChatGPT's published numbers. If math-heavy reasoning is your primary use case, run a 4-week pilot with your actual workload — vendor benchmarks don't predict your specific tasks.

3

General conversationChatGPT

English polish and instruction-following maturity. Memory across sessions. No political refusals on Western topics. DeepSeek refuses ~85% of politically sensitive queries (Tiananmen, Taiwan, Xi, Uyghurs) — bound for most enterprise use cases but worth knowing.

4

Long-form writingChatGPT

Canvas collaborative editor. GPT-5.5 Pro extended context. Better instruction following for stylistic guidance. DeepSeek viable but lacks the writing-workflow tooling.

5

Image / video / voice generationChatGPT

DALL-E + GPT-5 native image, Sora 2 video inside ChatGPT, Advanced Voice Mode. DeepSeek has none of these as of June 2026 — no shipping multimodal product.

6

Cost-sensitive batch inferenceDeepSeek (decisively)

V4-Pro output is ~34.5× cheaper than GPT-5.5; V4-Flash is ~107× cheaper. Cache hit pricing (~98% discount) compounds savings on agentic workloads with repeated context. For high-volume coding, document processing, batch translation, summarization, or embedding-style workloads, the cost differential dominates almost all other considerations.

7

Enterprise procurement (US Fortune 500)ChatGPT

DeepSeek banned on US government devices (Navy, NASA, multiple states); proposed federal bill would extend to all US gov devices. F500 procurement is risk-averse — even though private commercial use remains legal, most enterprise procurement boards reject Chinese-hosted AI. The open-weight workaround (Together AI / Fireworks in US datacenters) mitigates data sovereignty for some procurement teams.

8

Research / agent workloadsChatGPT (DeepSeek viable if self-hosted on US infra)

ChatGPT Agent Mode is the most mature agentic product (visual browser, terminal, Gmail/GitHub). DeepSeek catching up on tool-use; can be deployed via open-weight runs on Together / Fireworks / DeepInfra / NVIDIA NIM with US data residency.

The cost difference — what's real, what's a caveat

The 28–107× cost ratios are real. V4-Pro output at $0.87/M vs GPT-5.5 at $30/M = 34.5×. V4-Flash output at $0.28/M vs GPT-5.5 at $30/M = 107×. Cache hit pricing (~98% discount on cached input) compounds the savings on agentic workloads with repeated context. For batch inference at scale, the cost differential dominates almost all other considerations.

Caveats that narrow the gap:

  • Tool maturity gap. GPT-5.5 has Function Calling v2, Agent Mode, Codex — mature tooling that DeepSeek is catching up to. If your workload depends on these, switching to DeepSeek requires engineering work that eats into the cost savings.
  • Latency variance. DeepSeek's direct API has experienced rate limits during peak periods. For latency-sensitive production workloads, US-hosted open-weight providers (Together AI, Fireworks, DeepInfra) offer more predictable performance but at premium pricing above DeepSeek's direct API.
  • Western host premiums. Together AI / Fireworks / DeepInfra pricing for V4-Pro and V4-Flash is higher than DeepSeek's direct API — typically 2–4× DeepSeek's direct pricing. Still much cheaper than GPT-5.5, but the ratio compresses.
  • Total cost of ownership. Add ops overhead for managing the open-weight stack, monitoring, fallbacks, and retraining — the TCO gap is smaller than the headline API pricing suggests, though still substantial for high-volume workloads.

When DeepSeek wins on cost decisively: high-volume batch inference (document processing, code review, translation, summarization, embedding-style workloads), agentic workflows with high cache-hit rates, cost-sensitive production where latency and tool maturity gaps can be engineered around.

The open-weight workaround — US-hosted DeepSeek

DeepSeek V4-Pro and V4-Flash are MIT-licensed open weights. Commercial use, modification, redistribution, and fine-tuning are all permitted with no restriction. This makes DeepSeek the only open-weight flagship competitive with frontier closed models on SWE-bench Verified.

Available on US-hosted infrastructure:

  • Together AI — together.ai/models/deepseek-v4-pro
  • Fireworks AI — fireworks.ai (V4 endpoints live)
  • DeepInfra — V4-Pro and V4-Flash endpoints
  • NVIDIA NIM — build.nvidia.com/deepseek-ai/deepseek-v4-pro
  • Atlas Cloud — V4 hosting
  • Self-hosted — open weights on Hugging Face (deepseek-ai/DeepSeek-V4-Pro and DeepSeek-V4-Flash)

This is the standard enterprise workaround for procurement teams concerned about Chinese data-sharing laws. Data flows to US datacenters, not to DeepSeek's servers. Pricing on these hosts is 2–4× DeepSeek's direct API but still much cheaper than GPT-5.5. For US Fortune 500 procurement teams that can't approve DeepSeek's direct API, the open-weight US-hosted path is often workable.

Important caveat: the political censorship persists across all hosts because it's baked into post-training weights, not server-side. See the censorship section below.

Geopolitical procurement — what's actually banned

US government device bans (verified June 2026): US Navy, NASA, and multiple state agencies have prohibited DeepSeek on government-issued devices. A proposed federal bill would extend the ban to all US government devices within 60 days of passage. Australia, Taiwan, and South Korea have issued similar restrictions on government use.

NOT banned for US private and commercial use. Fortune 500 companies, startups, individual developers can all legally use DeepSeek. The bans are scoped to government data sovereignty concerns — Chinese data-sharing laws apply to DeepSeek's hosted infrastructure — not the underlying technology.

Standard enterprise procurement workaround: use DeepSeek's open-weight models on US-hosted infrastructure (Together AI, Fireworks, DeepInfra, NVIDIA NIM). This mitigates data sovereignty concerns while preserving the cost advantage. Most F500 procurement teams approve this path; few approve DeepSeek's direct China-hosted API for production workloads.

The $7.4B June 2026 funding round complicates things slightly. The round closed at $52–59B post-money. Investors include Tencent (~$10B), CATL (~$5B), and a Chinese state fund. The state fund received voting rights; other investors did not. CEO Liang Wenfeng contributed ~$3B himself and locked governance via a limited-partnership structure (5-year lockup, no investor voting power except the state fund). For some procurement teams, the state fund involvement is a red flag; for others, the well-capitalized trajectory legitimizes DeepSeek as a long-term player.

Censorship — baked into the weights

chat.deepseek.com and DeepSeek's hosted API refuse ~85% of politically sensitive queries on Chinese topics (Tiananmen, Xi, Taiwan, Uyghurs). Standard refusal pattern: “Sorry, that's beyond my current scope.” Taiwan-related outputs regurgitate PRC official positioning.

Critical: the same refusal patterns persist on Together AI, Fireworks, DeepInfra, and NVIDIA NIM runs. Censorship is baked into post-training weights, not server-side. Choosing a US-hosted provider mitigates data sovereignty but does NOT mitigate political censorship. Some patterns are partially bypassable with prompting tricks, but enterprise should not assume neutral political output on China-related topics.

For most enterprise use cases, censorship doesn't bind. Coding, math, business writing, document summarization, batch inference, customer support automation — these workloads rarely touch politically sensitive content. For research, journalism, geopolitical analysis, international news monitoring, or any workload that needs neutral output on China-related topics, the censorship is a real constraint.

No evidence V4 changed this behavior vs prior DeepSeek versions. The censorship is a stable feature of DeepSeek-trained models as of June 2026.

For SEO and GEO — does DeepSeek matter?

For Western brand visibility — generally no. DeepSeek does have a web-search mode on chat.deepseek.com, and the product has 127M monthly active users as of March 2026. But the user base is heavily Chinese-market. DeepSeek's web-search citation behavior hasn't been independently studied at scale the way ChatGPT (Profound 34,234-response study, 5W Q1 2026 audit) and Perplexity (BrightEdge, MarGen) have.

For Western brands — B2B SaaS, US/EU e-commerce, English-language publishers — optimizing for ChatGPT citations remains the priority. Verified ChatGPT citation patterns: 7.92 citations per response (Profound), Wikipedia 47.9% of top-10 share, Reddit jumped 87% from July 2025 baseline. See our ChatGPT SEO guide for engine-specific tactics.

For brands serving China-market customers specifically, DeepSeek optimization may matter. For everyone else, focus on the engines whose users are actually your buyers — ChatGPT (900M WAU), Perplexity (100M MAU, B2B research-stage), Claude (enterprise-heavy via Anthropic's 70% Fortune 100 penetration), and Google (Gemini + AI Overviews).

Recent Q2 2026 product news

April 23, 2026: GPT-5.5 becomes default ChatGPT model.

April 24, 2026: DeepSeek V4-Pro + V4-Flash preview shipped, MIT license, top Hugging Face trending slot.

May 2026: Bloomberg reports Liang Wenfeng declares AGI goal as $10B round advances.

May 5, 2026: GPT-5.5 Instant becomes new default for ChatGPT.

June 3, 2026: CNBC reports DeepSeek $7B round forming.

June 8, 2026: OpenAI files for IPO.

June 2026: DeepSeek closes $7.4B round at ~$52–59B post-money (Tencent + CATL + Chinese state fund with voting rights; Liang Wenfeng largest single contributor at ~$3B).

R2: NOT yet released. Liang Wenfeng reportedly dissatisfied with performance; analyst forecasts slipped to Q3–Q4 2026.

4 common misconceptions debunked

DeepSeek is just a ChatGPT clone

False

Distinct architecture (MoE with Compressed Sparse Attention + Heavily Compressed Attention), original RL pipeline (R1 reasoning training methodology was novel in January 2025), different training data mix. V4 is genuinely frontier-class open weights, not a clone. The $7.4B June 2026 funding round (Tencent + CATL + Chinese state fund) validates the trajectory — DeepSeek has matured from one-time shock into legitimate frontier lab.

DeepSeek is unsafe for enterprise

Nuanced — depends on hosting

chat.deepseek.com and DeepSeek's hosted API: real data sovereignty risk for US firms (data flows to Chinese infrastructure; PRC data-sharing laws apply). Open-weight runs on Together AI, Fireworks, DeepInfra, NVIDIA NIM in US datacenters mitigate this — weights run on US infra without traffic to DeepSeek's servers. Political censorship persists across all hosts because it's baked into post-training weights, but for most enterprise use cases (coding, math, business writing) this doesn't bind.

DeepSeek wins on coding

Partially true

SWE-bench Verified 80.6% is competitive with the top closed models. SWE-bench Pro favors GPT-5.5 (58.6% vs ~55.4% third-party V4) — and Claude Opus 4.8 wins the category outright at 69.2%. Where DeepSeek wins decisively is cost-per-correct-PR: at 28–107× cheaper output, you can run more attempts, more variants, more reviews for the same budget.

DeepSeek is illegal to use in the United States

False

Banned on certain US government devices (Navy, NASA, multiple states; proposed federal bill would extend within 60 days of passage). Private and commercial use remains fully legal. Most enterprise procurement teams treat it as risk-averse rather than legally blocked — the open-weight workaround on US-hosted infra is the standard mitigation.

Frequently asked questions

Is DeepSeek really 30× cheaper than ChatGPT?+
Yes, on API output costs — and even more on V4-Flash. DeepSeek V4-Pro: $0.435 input / $0.87 output per 1M tokens. GPT-5.5 API: $5 input / $30 output per 1M tokens. V4-Pro is ~34.5× cheaper on output. V4-Flash at $0.14/$0.28 is ~107× cheaper than GPT-5.5. Cache hit pricing (~98% discount on cached input) compounds the savings on agentic workloads. Caveats: tool maturity gap (GPT-5.5 has Function Calling v2, Agent Mode, Codex — DeepSeek catching up), latency variance on DeepSeek's direct API during peak periods, and Western open-weight hosts (Together AI / Fireworks) charge premiums above DeepSeek's direct pricing. The cost differential is real and substantial; total-cost-of-ownership gap narrows after tool integration and ops overhead.
Can US companies use DeepSeek legally?+
Yes for private and commercial use. The bans are scoped to government devices: US Navy, NASA, and multiple state agencies have prohibited DeepSeek on government-issued devices. A proposed federal bill would extend the ban to all US government devices within 60 days of passage. Private companies, including Fortune 500, can legally use DeepSeek — though most enterprise procurement teams are risk-averse and require additional review. The standard enterprise workaround is to run DeepSeek's open-weight models (V4-Pro and V4-Flash are MIT-licensed) on US-hosted infrastructure via Together AI, Fireworks, DeepInfra, or NVIDIA NIM. This mitigates data sovereignty concerns while preserving the cost advantage.
Is DeepSeek banned in the United States?+
Partially. As of June 2026, DeepSeek is banned on US Navy, NASA, and multiple state-level government devices. A proposed federal bill would extend the ban to all US government devices within 60 days of passage. Australia, Taiwan, and South Korea have issued similar restrictions on government use. Private companies and individuals can legally use DeepSeek in the US. The bans target government data sovereignty concerns — Chinese data-sharing laws apply to DeepSeek's hosted infrastructure — not the underlying technology.
Does DeepSeek work as well as GPT-5.5 for coding?+
On verified benchmarks, partially. SWE-bench Verified: DeepSeek V4-Pro 80.6% (tied Gemini 3.1 Pro for top open-weight) — competitive with frontier closed models. SWE-bench Pro: GPT-5.5 wins at 58.6% vs V4-Pro's ~55.4% (third-party; DeepSeek has no official Scale SEAL entry). Claude Opus 4.8 wins SWE-bench Pro outright at 69.2%. Where DeepSeek wins decisively is cost-per-correct-PR — at 28–107× cheaper output, you can run more attempts and reviews for the same budget. For multi-file code and complex PR resolution, ChatGPT (or Claude) wins on accuracy; DeepSeek wins on economics.
What about R2 — should I wait for it?+
Don't wait. DeepSeek R2 has NOT been released as of June 2026, despite multiple rumored launches throughout the year. CEO Liang Wenfeng has reportedly expressed dissatisfaction with R2's performance; analyst forecasts have slipped R2 to Q3 or Q4 2026. The current DeepSeek lineup (V4-Pro, V4-Flash, R1) covers the reasoning use case via V4's hybrid thinking modes (Non-think / Think-High / Think-Max). If you need DeepSeek for reasoning work today, V4-Pro with Think-Max is the available option.
Does running DeepSeek on Together AI solve the data privacy concern?+
Yes for data sovereignty — but not for political censorship. Together AI, Fireworks, DeepInfra, and NVIDIA NIM host DeepSeek's open-weight models on US infrastructure. Data flows to US datacenters, not to DeepSeek's servers in China. This is the standard enterprise workaround for procurement teams concerned about Chinese data-sharing laws. However: political censorship is baked into the post-training weights — the same refusal patterns on Tiananmen, Xi, Taiwan, and Uyghurs persist regardless of which US-hosted provider runs the model. For most enterprise use cases (coding, math, business writing), the censorship doesn't bind. For research, journalism, or geopolitical analysis, it's a real constraint.
Is the political censorship in DeepSeek a real problem for my business?+
Depends on your use case. For coding, math, business writing, document summarization, batch inference, customer support automation — censorship rarely binds because these workloads don't touch politically sensitive topics. For research, journalism, geopolitical analysis, international news monitoring, or any workload that needs neutral output on China-related topics — it's a real constraint. Independent testing studies report ~85% refusal rate on politically sensitive queries (Tiananmen, Xi, Taiwan, Uyghurs). The refusal pattern is baked into post-training weights and persists on Together AI / Fireworks / DeepInfra runs — it's not a hosting choice, it's a model property.
Should my Fortune 500 procurement team approve DeepSeek?+
Depends on workload and hosting choice. For pure batch inference workloads (high-volume document processing, code review, summarization) where cost dominates, the case for DeepSeek is strong — especially via Together AI or Fireworks in US datacenters. For consumer-facing or politically sensitive content, the censorship and procurement perception risks may exceed the cost savings. Most F500 procurement teams in mid-2026 require: (1) hosting on US infrastructure (Together / Fireworks / DeepInfra / NVIDIA NIM, not DeepSeek's direct API), (2) workload review to ensure no politically sensitive content paths, (3) clear documentation of the model's open-weight status and MIT license. The $7.4B June 2026 funding round (Tencent + CATL + Chinese state fund with voting rights) reinforces some procurement teams' caution; others see legitimacy validation.
How does DeepSeek's $7.4B funding round change my decision?+
Mixed signal. The $7.4B round (closed June 2026 at ~$52–59B post-money) is DeepSeek's first external funding and validates the trajectory — DeepSeek has matured from one-time shock (January 2025) into a legitimate frontier-class lab. CEO Liang Wenfeng contributed ~$3B himself and locked governance via a limited-partnership structure (5-year lockup, no investor voting power except a Chinese state fund). The state fund's voting rights are the procurement red flag for some F500 teams. Investors include Tencent (~$10B) and CATL (~$5B). For technical decisions, the funding round doesn't change V4's capabilities. For procurement decisions, it reinforces both the legitimacy case (well-funded, well-governed) and the caution case (Chinese state fund involvement).
Should I optimize my SEO for DeepSeek citations?+
For Western brands, generally no. chat.deepseek.com has roughly 127M monthly active users as of March 2026, but the user base is heavily Chinese-market. DeepSeek's web-search citation behavior hasn't been independently studied at scale the way ChatGPT (Profound 34,234-response study, 5W Q1 2026 audit) and Perplexity (BrightEdge, MarGen) have. For Western brand visibility — B2B SaaS, US e-commerce, English-language publishers — optimizing for ChatGPT citations remains the priority (verified 7.92 citations per response, Wikipedia 47.9% of top-10 share). See our /chatgpt-seo guide for engine-specific tactics. If your business serves China-market customers specifically, DeepSeek optimization may matter; for everyone else, focus on the engines whose users are actually your buyers.

Sources

  1. DeepSeek V4-Pro on Hugging Face (MIT license open weights). huggingface.co/deepseek-ai/DeepSeek-V4-Pro
  2. DeepSeek API news — V4 release April 24, 2026. api-docs.deepseek.com/news/news260424
  3. DeepSeek pricing (verified live URL). deepseek.ai/pricing
  4. DeepSeek API updates. api-docs.deepseek.com/updates
  5. Hugging Face — DeepSeek V4 blog. huggingface.co/blog/deepseekv4
  6. NVIDIA NIM — DeepSeek V4-Pro model card. build.nvidia.com/deepseek-ai/deepseek-v4-pro
  7. Together AI — DeepSeek V4-Pro endpoint. together.ai/models/deepseek-v4-pro
  8. TechTimes — DeepSeek closes $7.4B round (June 16, 2026). techtimes.com
  9. CNBC — DeepSeek $7B round forming (June 3, 2026). cnbc.com
  10. SCMP — How DeepSeek's funding secures Liang Wenfeng's grip. scmp.com
  11. Computer Weekly — US lawmakers move to ban DeepSeek (federal bill). computerweekly.com
  12. Conference Board — State and federal governments DeepSeek bans. conference-board.org
  13. OpenAI — accelerating the next phase ($852B March 2026 raise). openai.com/index/accelerating-the-next-phase-ai
  14. OpenAI — GPT-5.5 release (April 23, 2026). openai.com/index/introducing-gpt-5-5
  15. TechCrunch — ChatGPT 900M WAU (Feb 27, 2026). techcrunch.com
  16. SWE-bench leaderboard. swebench.com
  17. Profound — ChatGPT citation patterns (Wikipedia 47.9% top-10 share). tryprofound.com/blog

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