DeepSeek vs Competitors Review: Prices, Published Benchmarks and Trade-offs

If you have a budget committee asking why your team pays OpenAI or Anthropic four-figure API bills while a Chinese lab is shipping open-weight models at a fraction of the price, this DeepSeek vs competitors review is the document to hand them. We compare DeepSeek’s current API models, deepseek-flash (DeepSeek-V4.1-Flash-1017) and deepseek-v4-pro (DeepSeek-V4-Pro-0813), with GPT-5.4/5.5, Claude Opus 4.6/4.7 and Gemini 3.1 Pro Preview using official pricing as of 22 October 2026, documentation and vendor-reported benchmarks; the site does not run its own evaluations.

Our verdict at a glance

Our assessment, based on the sources listed below:

On listed price, both DeepSeek models undercut every competitor row in our pricing table, and off-peak hours halve DeepSeek’s rates, so the cost case favours DeepSeek for high-volume coding agents and long-context work. On quality, the like-for-like evidence cited here is DeepSeek’s own: mixed on coding, behind on expert reasoning. Data location is the strongest reason to stay with an incumbent.

Who should use DeepSeek — and who shouldn’t

Switch to DeepSeek if you run agentic coding or batch workloads at scale, can shift flexible jobs off-peak, process long documents (both models list a 1M-token context and 384K maximum output) or need open weights you can self-host.

Stay with an incumbent if your own evaluations show a quality gap that matters, if your compliance posture cannot accept personal data stored in China, or if you depend on another vendor’s first-party integration. For a deeper read on those trade-offs, our DeepSeek review covers the product end-to-end.

How we assessed it

This review compares published evidence; the site does not run its own evaluations. Figures come from the dated sources below or from arithmetic on them, and vendor-reported results are labelled (competitor scores in DeepSeek’s tables are DeepSeek’s numbers). All were read on 22 October 2026:

• DeepSeek Models & Pricing (last modified 19 October 2026) and the API docs home; API Change Log entries of 24 April, 13 August, 10 September and 17 October 2026; the V4 Preview (24 April), V4-Flash-Vision-Exp (21 August), V4.1-Flash (10 September) and V4.1-Flash-1017 (17 October 2026) release notes.
• The DeepSeek-V4 technical report (arXiv 2606.19348, 26 April 2026); the V4-Pro (24 April), V4-Pro-0813 (13 August) and V4.1-Flash-1017 (17 October 2026) model cards.
• DeepSeek’s Thinking Mode, Responses API, Multi-round Conversation and Search & Fetch guides; its privacy policy (updated 10 February 2026) and Open Platform Terms of Service (effective 29 April 2026).
• The OpenAI, Anthropic and Google Gemini pricing pages; OpenAI’s deprecations page; Anthropic’s models overview; the Italian data-protection authority’s press release of 30 January 2025.

Head-to-head: coding

Coding is where DeepSeek’s April 2026 comparison was most competitive. In the V4 technical report, V4-Pro-Max (Preview, maximum reasoning effort) scored 80.6% on SWE-Bench Verified against 80.8% for Claude Opus 4.6, 93.5% on LiveCodeBench against 88.8%, and 67.9% on Terminal Bench 2.0 against 65.4%, with GPT-5.4 on 75.1% and Gemini 3.1 Pro on 68.5%. The same report said the open models V4-Pro, Kimi K2.6 and GLM-5.1 “still lag behind their closed-source counterparts” on code-agent tasks, and on 30 internal R&D tasks it gave V4-Pro-Max 67% against 80% for Opus 4.6.

DeepSeek’s newer model cards are mixed. V4-Pro GA (13 August 2026) led Claude Opus 4.8 on Terminal Bench 2.1 (87.9 vs 85.0) and DeepSWE (62.7 vs 58.0), where Claude Fable 5 (“Fable-5 (w/ fallback)” on the card) scored higher still (88.0 and 70.0), but trailed Opus 4.8 on NL2Repo (61.5 vs 69.7) and DeepSeek’s two internal DSBench sets. V4.1-Flash-1017 (17 October 2026) led GPT-5.6 Sol on all four coding rows and Claude Opus 5 on DeepSWE v1.1 (77.0 vs 74.0), but trailed Opus 5 on Terminal-Bench 4.0 (42.3 vs 51.8), ProgramBench (29.1 vs 37.0) and NL2Repo-Bench (68.2 vs 75.3).

In April, V4-Flash-Max scored 79.0% on SWE-Bench Verified, 1.6 points below Pro; V4-Flash was retired on 10 September 2026, and DeepSeek claims its successor benchmarks ahead of V4-Pro. deepseek-v4-pro costs 3.3× as much as deepseek-flash on output and 4.4× on cache-miss input, and Flash allows 2,500 concurrent requests against 500. See our DeepSeek for coding guide for prompt patterns.

Head-to-head: reasoning

On expert-level reasoning, DeepSeek’s April 2026 comparison showed the closed models ahead. V4-Pro-Max scored 37.7 on HLE (Humanity’s Last Exam, without tools) against 40.0 for Claude Opus 4.6, 39.8 for GPT-5.4 and 44.4 for Gemini 3.1 Pro, and 90.1 on GPQA Diamond against 91.3 to 94.3; on factual recall (SimpleQA-Verified), its 57.9 trailed only Gemini’s 75.6.

Later DeepSeek figures are higher but, on HLE without tools, still trail every closed model listed: 42.7 for V4-Pro GA against 49.8 for Claude Opus 4.8 and 53.3 for Claude Fable 5 (13 August 2026), and 42.6 for V4.1-Flash-1017 against 56.3 for Claude Opus 5 and 44.5 for GPT-5.6 Sol (17 October 2026). Newer models such as Claude Opus 5.5, GPT-6 Astra and Gemini 4 Argon are not in these tables, so we treat expert-reasoning parity as unproven. Our DeepSeek V4-Pro page tracks the full benchmark table as new numbers land.

Head-to-head: writing

None of the benchmark tables cited here measures English long-form writing. The closest evidence is the Chinese-writing evaluation in DeepSeek’s April 2026 technical report: V4-Pro (Preview) beat Gemini 3.1 Pro on functional writing (62.7% vs 34.1% win rate) and creative writing, while Claude Opus 4.5 led on the hardest prompts (52.0% vs 45.9%). We draw no writing-quality ranking from that. On cost, deepseek-flash is the cheaper starting point for everyday drafting. See DeepSeek for writing for workflow examples.

Head-to-head: pricing

Per 1M tokens as of 22 October 2026: DeepSeek rows show peak / off-peak; competitor rows show standard rates for the shortest-context tier.

Both DeepSeek models undercut every row above in both periods, but that is not a blanket “cheapest” claim: GPT-4o mini ($0.15 / $0.60; deprecated, shutdown 14 January 2027) is below deepseek-flash’s peak cache-miss input and output rates and equal to its off-peak ones (Flash’s cached input, $0.006 / $0.003, is far below GPT-4o mini’s $0.075), and GPT-5.4 nano ($0.20; deprecated, shutdown 1 April 2027) and Gemini 3.1 Flash-Lite ($0.25) undercut Flash’s $0.30 peak input rate. Nor are these the vendors’ newest models: Anthropic lists Opus 4.6 and 4.7 as legacy, and GPT-5.4 and GPT-5.5 are no longer OpenAI’s featured flagships. Long prompts widen the gap with GPT-5.4, which charges $5.00 / $22.50 above 272K input tokens; DeepSeek’s rates do not change with prompt length.

Off-peak rates have been half the peak rates since 16:00 UTC on 16 August 2026. Peak hours are 01:00–04:00 and 06:00–10:00 UTC, Monday to Friday, excluding Chinese public holidays; all other hours, including weekends, are off-peak, so 35 of the 168 hours in a normal week are peak.

Worked example (illustrative): 1,000,000 calls per month on deepseek-flash

Assume a 2,000-token cached system prompt, a 200-token user message (cache miss) and a 300-token response per call:

                                                   Peak       Off-peak
Cache hit : 2,000,000,000 ÷ 1,000,000 × $0.006 =  $12.00   (× $0.003 =   $6.00)
Cache miss:   200,000,000 ÷ 1,000,000 × $0.30  =  $60.00   (× $0.15  =  $30.00)
Output    :   300,000,000 ÷ 1,000,000 × $1.20  = $360.00   (× $0.60  = $180.00)
Total (deepseek-flash)                           $432.00               $216.00

On deepseek-v4-pro (tokens in millions): 2,000 × $0.044 + 200 × $1.32 + 300 × $3.96 = $88.00 + $264.00 + $1,188.00 = $1,540.00 at peak; 2,000 × $0.022 + 200 × $0.66 + 300 × $1.98 = $44.00 + $132.00 + $594.00 = $770.00 off-peak. On GPT-5.4 at standard short-context rates: 2,000 × $0.25 (cached) + 200 × $2.50 + 300 × $15.00 = $500.00 + $500.00 + $4,500.00 = $5,500.00. That is 12.7× the Flash peak bill and 25.5× the off-peak bill; totals are exact, ratios rounded to one decimal. That spread is why the DeepSeek pricing calculator is worth running before you budget.

Head-to-head: privacy

This is the hardest trade-off, and for many regulated buyers it favours the incumbents. DeepSeek’s privacy policy (updated 10 February 2026) says it collects, processes and stores personal data “in People’s Republic of China”, and its Open Platform Terms of Service, which cover API use, are governed by mainland Chinese law. On 30 January 2025, Italy’s data-protection authority ordered an urgent limitation on DeepSeek’s processing of Italian users’ data (Garante press release). For the US picture, see our rundown of DeepSeek US restrictions.

The counter-move: DeepSeek publishes weights on Hugging Face, MIT-licensed per their model cards, for the V4 Preview, DeepSeek-V4-Pro-0813 (behind deepseek-v4-pro) and DeepSeek-V4.1-Flash-1017, so you can run the current models on your own infrastructure or with a regional provider. If self-hosting is not an option, the incumbents’ regional options may be easier to defend: OpenAI charges a 10% data-residency uplift on GPT-5.4 and 5.5, and Anthropic 1.1× for US-only inference (pricing pages, as of 22 October 2026).

Head-to-head: ecosystem

The incumbents offer first-party apps and tools such as OpenAI’s Codex and Anthropic’s Claude Code. DeepSeek’s documented surface is narrower: web chat and mobile apps; an API in OpenAI Chat Completions, OpenAI Responses (native support, with a Codex setup) and Anthropic formats; image input on deepseek-flash plus a free Files API; Search and Fetch APIs at $0.75 per 1,000 successful requests each, open to everyone since 17 October 2026; DeepSeek Harness (developer preview); and an Agent Integrations guide covering Claude Code, Codex, OpenCode and other tools.

With the OpenAI Python SDK you change the base_url, the API key and the model name; DeepSeek-specific fields such as thinking go in extra_body:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.deepseek.com",
    api_key="sk-...",
)

resp = client.chat.completions.create(
    model="deepseek-v4-pro",
    messages=[{"role": "user", "content": "Plan the migration."}],
    reasoning_effort="high",
    extra_body={"thinking": {"type": "enabled"}},
)

Chat requests hit POST /chat/completions; the API is stateless, so clients resend the conversation history with every request. Thinking mode is a parameter on both models, not a separate model ID: it is on by default at effort "high" (low, high or max), is turned off with {"thinking": {"type": "disabled"}}, ignores temperature, presence_penalty and frequency_penalty, and returns reasoning_content alongside content. More in our DeepSeek API documentation notes.

Current vs legacy model IDs

The current model IDs are deepseek-flash (DeepSeek-V4.1-Flash-1017) and deepseek-v4-pro (DeepSeek-V4-Pro-0813). The legacy deepseek-chat and deepseek-reasoner IDs were retired (scheduled for 24 July 2026 at 15:59 UTC); until then they routed to deepseek-v4-flash in non-thinking and thinking mode respectively. deepseek-v4-flash and deepseek-v4-flash-vision-exp are still accepted, but those models are retired: requests are served by DeepSeek-V4.1-Flash-1017 at the Flash price. Migration is a model= swap with the same base_url; code that used deepseek-chat should also disable thinking to keep non-thinking behaviour.

When to pick which

• Pick deepseek-flash (DeepSeek-V4.1-Flash-1017) for chat, customer support, classification, long-context summarisation and most coding assistance: it is the cheaper default and the DeepSeek model that accepts images.
• Pick deepseek-v4-pro (DeepSeek-V4-Pro-0813) for workflows already validated on it, such as agentic coding or multi-step tool use, where 3.3× Flash’s output price is acceptable; the Responses API’s server-side web_search tool works only with Pro. DeepSeek dropped a planned V4-Pro phase-out in September 2026.
• Pick GPT-5.4 or GPT-5.5 if your own evaluations favour them; GPT-5.4 had the top Terminal Bench 2.0 and HMMT 2026 scores in DeepSeek’s April table.
• Pick Claude Opus 4.6/4.7 (legacy, $5 / $25 as of 22 October 2026) if your evaluations favour them or you need US-only inference; Opus 4.6 led on SWE-Bench Verified and 1M-token retrieval in DeepSeek’s April table.
• Pick Gemini 3.1 Pro Preview ($2.00 / $12.00 up to 200K tokens, as of 22 October 2026) if your evaluations favour it; it led on HLE, GPQA Diamond and SimpleQA-Verified in DeepSeek’s April table.

Competitor context

For the full pairwise matchups, see our DeepSeek vs ChatGPT and DeepSeek vs Claude comparisons, or browse every head-to-head we maintain in the DeepSeek comparisons hub.

DeepSeek AI Guide is an independent resource and is not affiliated with DeepSeek or its parent company. Model IDs, pricing and API behaviour change; check the official DeepSeek documentation and pricing page before committing to a production decision.