Qwen vs DeepSeek

Quick verdict

Qwen (Alibaba’s model family) is the broader open-weight ecosystem: dense sizes you can run on one GPU, long multimodal context (text + image/video on Plus-class and VL/Omni SKUs), Qwen Studio chat, Qwen Code in the terminal, and a thick Alibaba Cloud Model Studio catalog (Max / Plus / Flash / VL / Omni / Coder). DeepSeek is the sharper bet on frontier-adjacent text reasoning at rock-bottom hosted prices: V4 Flash and V4 Pro on a simple official rate card, MIT open weights, OpenAI- and Anthropic-compatible APIs, and free consumer chat.

Pick Qwen if you need multimodal inputs, a ladder of local models, or agent workflows that mix screenshots, long docs, and Chinese/English product surfaces. Pick DeepSeek if your workload is text-heavy (coding, math, RAG, agents) and you care most about $/token simplicity, MIT licensing, and cache-friendly API bills. Serious teams often route both—or self-host open weights when compliance forbids China-hosted APIs.

Side-by-side

What each product is in 2026

Qwen is Alibaba’s multi-generation LLM brand: open research releases (Qwen3 / 3.5 / 3.6 dense and MoE), specialized lines (Coder, VL, Omni, ASR), consumer Qwen Studio, developer Qwen Code, and paid inference on Alibaba Cloud Model Studio (OpenAI-compatible). The thesis is coverage—many sizes, many modalities, Chinese + global cloud regions (Singapore, Beijing, US, EU, Hong Kong, Japan), and enough open weights that local-LLM communities treat Qwen as a default “family” rather than a single checkpoint.

DeepSeek is a Hangzhou lab that punched above its marketing budget with R1/V3 and, in 2026, V4 Flash / V4 Pro: million-token context, thinking mode (default on), tool calling, JSON mode, FIM and prefix-completion betas, and official prices that still undercut most Western frontier APIs by an order of magnitude on raw text. Release notes position Pro at roughly 1.6T total / 49B active params and Flash at roughly 284B total / 13B active. The thesis is efficiency—publish open weights under MIT, keep the hosted API simple, and win volume workloads where token count is the bill.

Pricing and real cost (TCO)

Both look “cheap vs GPT/Claude.” The bill that bites is agent loops (huge input re-sends), wrong tier choice (Max/Pro for work Flash could do), and subscription credit burn on Alibaba token/coding plans.

DeepSeek (official API)

• deepseek-v4-flash — $0.14 / 1M input (cache miss), $0.0028 cache hit, $0.28 / 1M output; concurrency limit 2500.
• deepseek-v4-pro — $0.435 / 1M input (cache miss), $0.003625 cache hit, $0.87 / 1M output; concurrency 500. (Some secondary writeups still quote older list/promo pairs such as $1.74/$3.48 before discount—always re-check the official Models & Pricing page for the live row.)
• Context / output — 1M context; max output 384K tokens.
• Compatibility — base URL https://api.deepseek.com (OpenAI format) and https://api.deepseek.com/anthropic (Anthropic-compatible).
• Consumer — Free chat at chat.deepseek.com; API is prepaid balance on platform.deepseek.com.

Qwen (Model Studio + products)

• Pay-as-you-go — Official Alibaba Cloud tables are long: Max / Plus / Flash / Turbo / VL / Omni / Coder, thinking vs non-thinking, and tiered rates by input length (e.g. 0–256K vs 256K–1M). Region (Singapore international vs Beijing vs US Virginia, etc.) changes the number.
• International Max-class — qwen3.7-max list often shown at $2.5 input / $7.5 output per 1M with limited-time discounts (e.g. 50% off list) on some console rows.
• Plus (intl example) — qwen3.7-plus list around $0.4 / $1.6 per 1M for ≤256K context (thinking output priced in the same band as non-thinking on recent rows), jumping for 256K–1M; promos (e.g. 20% off) appear periodically.
• Flash (intl example) — qwen3.6-flash around $0.25 / $1.5 (≤256K); older qwen3.5-flash / qwen-flash SKUs can be lower still. Use Flash for volume unless Max quality is required.
• Coding Plan — Flat monthly for IDE-style tools with Qwen + select peers (Kimi, GLM, MiniMax on recent plan docs). Pro-class marketing and docs commonly cite ~$50/month with request ceilings on the order of 6,000 / 5 hours, 45,000 / week, 90,000 / month. Older cheaper Lite tiers were discontinued for new subscriptions (Mar 2026) and renewals (Apr 2026)—do not plan a $10 Lite path if you are new.
• Token Plan (Team) — Credit seats such as Standard ~$30, Pro ~$100, Max ~$200 per seat/month (with monthly credit allotments). Power users report $30-class packs evaporating quickly on qwen3.7-max agentic coding—treat Max as a scalpel.
• Studio chat — Consumer Qwen Studio remains free; the separate developer OAuth free API tier ended around 2026-04-15.

TCO notes: Cache hits dominate DeepSeek agent economics—keep stable system prompts and tool schemas to hit the ~$0.003/M input band. On Qwen, region + context tier + model ID decide the invoice more than the brand name “Qwen.” Batch inference on some Model Studio SKUs is 50% of realtime; context cache discounts exist on many rows but do not stack with batch the way marketing sometimes implies. Third-party routers (OpenRouter, DeepInfra, etc.) can undercut or reshuffle list prices—verify the endpoint you actually call. Measure cost per merged PR or per successful agent run, not cost per million on a pricing page alone.

Models, licenses, and self-hosting

DeepSeek open path: V4 Pro and V4 Flash (and prior R1/V3 lines) publish weights on Hugging Face under MIT. Self-host if you need offline inference or to avoid sending prompts to DeepSeek’s servers. Full MoE still means multi-GPU or aggressive quant/SSD offload for home labs; HN and local communities note Flash’s lower active param count helps throughput, but RAM for the full expert bank remains the bottleneck.

Qwen open path: Qwen3 announced open MoE (e.g. 235B-A22B, 30B-A3B class) and a dense ladder (32B down to sub-1B) under Apache 2.0 for those releases; later 3.5/3.6 series continue the “many sizes” strategy that local users love. Flagship Max hosted SKUs are often closed weights—you buy inference, not a downloadable twin. VL and Omni lines extend the family into vision/audio where DeepSeek’s public API story stays text-centric.

Local reality: Independent cost writeups still default many teams to Qwen dense ~14B–32B-class on a single workstation GPU for code/chat, while full DeepSeek MoE remains multi-GPU territory unless heavily quantized. That single fact drives a lot of r/LocalLLaMA loyalty to Qwen even when DeepSeek wins hosted benchmarks.

Coding and agents

This fight is close and harness-dependent.

• Qwen ships dedicated Qwen3-Coder sizes and Qwen Code, an open terminal agent that can auth to Model Studio Coding Plan, OpenRouter, even DeepSeek as a provider. Multimodal Plus / VL models help when the bug is in a screenshot or UI video. Long-context Plus SKUs help multi-file triage past a few hundred thousand tokens.
• DeepSeek leans on general V4 quality + thinking mode + tool calls + FIM beta. Independent agent-routing writeups and SWE-bench-class scores put V4 Pro near frontier coding agents at a fraction of Western API cost; V4 Flash often wins pure $/quality for high-volume loops. HN anecdotes of multi-million-token sessions for cents are the product marketing you cannot buy.
• Hands-on 2026 writeups split tasks: DeepSeek often faster/cheaper on bounded one-shots and bulk text agents; Qwen sometimes stronger on multi-file refactors, idiomatic front-end, and multimodal agent steps. Older R1 vs Qwen3 notes gave R1 the hard-math edge and Qwen better everyday code structure—still a useful mental model, but re-bench on your harness quarterly.

Community sentiment (Reddit / HN)

Production split is the consensus. A widely discussed r/DeepSeek thread on Qwen 3.5 vs DeepSeek-V3 argued Qwen is the better all-around production pick (instruction following, long context, multimodal, agents) while DeepSeek remains excellent for pure text reasoning/coding and MIT simplicity—commenters still split, with some preferring DeepSeek outright and others bouncing off Qwen instruction-following in specific agent harnesses.

Local community loves Qwen’s ladder. r/LocalLLaMA repeatedly notes DeepSeek gets more hype while Qwen quietly offers models people can actually run; comparisons of Qwen3.6 / Qwen3-Coder / DeepSeek-Coder show task-type wins rather than a permanent champion. Dense Qwen on RAM often feels snappier than MoE offload to SSD.

HN economics favor DeepSeek API. Threads on V4 pricing and “almost on the frontier” praise cache-driven cost and open-weight exit options if a host bans you—while calling out missing image support. Separate discussions of Qwen free-tier discontinuation became a developer-community flashpoint when OAuth API free access died in April 2026.

Subscription pain is real on Qwen Max. Users report Alibaba token/coding credits vanishing quickly when pointed at top Max models during Claude-Code-like sessions. r/Qwen_AI threads on Coding Plan Lite discontinuation and $30 token packs burning in hours push daily drivers toward Plus/Flash or DeepSeek metered API.

Praise and complaints both ways: DeepSeek wins “I burned 2M tokens for 30¢” stories and “swap base URL, ship” integration simplicity; it loses on multimodal and on political/enterprise trust. Qwen wins “one family for vision + local + CN product”; it loses on SKU confusion, Max sticker shock, and fragmented pricing tables.

When Qwen wins

• You need image/video/audio understanding in the same model family (VL / Omni / multimodal Plus).
• You want a local dense model ladder (small → mid → large) under Apache-style open weights.
• Long-context agent work with mixed modalities and Alibaba/Qwen Code tooling.
• You are already on Alibaba Cloud regions, compliance paths, Coding Plan quotas, or Token Plan seats.
• Chinese-language product UX and Studio app distribution matter for end users.
• You want batch discounts, multi-region Model Studio endpoints, or OCR/VL specialty SKUs in one vendor catalog.

When DeepSeek wins

• Hosted text volume: coding agents, batch synthesis, math/reasoning loops where Flash $0.14/$0.28 dominates TCO.
• You want one simple official rate card and MIT weights as a hard fallback.
• Thinking-mode reasoning without buying a separate “o1-class” Western SKU.
• Team already standardized on OpenAI SDK / Anthropic-compatible tools—swap base URL and ship.
• Free chat acquisition for non-API users with optional upgrade to API later.
• High concurrency agent fleets (Flash concurrency limit is much higher than Pro on the official card).

Risks and failure modes

• Data residency / policy: DeepSeek’s privacy policy states personal data (including prompts/uploads) is stored on servers in the PRC. Multiple governments and agencies (Australia, Taiwan, various US federal/state entities, NASA/Navy reports, etc.) restricted or banned DeepSeek on government devices. Same diligence applies to Alibaba-hosted Qwen APIs. Self-host open weights for sensitive code and PII.
• Content filters: Hosted Chinese models show refusals or live redaction on politically sensitive topics. Do not use them as neutral political research oracles. Open weights reduce the live API filter layer but not training priors.
• SKU confusion (Qwen): Max vs Plus vs Flash vs region vs context tier can 5–10× the bill. Pin model IDs in code; read the active price row including thinking vs non-thinking columns.
• Alias churn (DeepSeek): chat/reasoner deprecations and thinking defaults surprise apps that assumed old behavior.
• Benchmark theater: Independent rollups give Qwen edges on some agentic/long-ctx suites and DeepSeek edges on pure math/price-performance—your harness may reverse that monthly.
• Self-host cost: “Open” ≠ free. Multi-GPU MoE power, engineering time, and quant quality dwarf API fees at low volume.
• Subscription traps: Token plans + Max model + agentic IDE = surprise empty balance. Coding Plan Lite retirement stranded some budget paths.
• Third-party host variance: OpenRouter/DeepInfra prices and latency are not identical to first-party—log which provider served each request.

Recommendation by profile

FAQ

Is Qwen better than DeepSeek in 2026?
For multimodal, local dense sizes, and Alibaba product tooling—usually Qwen. For cheapest hosted text reasoning/coding at scale—usually DeepSeek. “Better” is a workload word; bake off on your eval set.

How much cheaper is DeepSeek’s API?
Official V4 Flash is $0.14 / $0.28 per 1M tokens (cache miss). Qwen Max international list is often around $2.5 / $7.5 before promos; Qwen Plus/Flash narrow the gap a lot. Always compare the SKUs you will actually call, including region and context tier.

Are the models open source?
DeepSeek V4 weights: MIT on Hugging Face. Qwen open releases: commonly Apache 2.0 for listed dense/MoE cards; some top hosted Max models are not open weights. Check the specific model card.

Which is better for coding agents?
Both. Prefer Qwen when multimodal context and Qwen Code/Coding Plan matter; prefer DeepSeek when pure text agent loops and unit cost dominate. A one-week bake-off on your repo beats leaderboard screenshots.

Can I run them locally?
Yes for open-weight variants. Qwen’s smaller dense models are the practical single-GPU path; full DeepSeek MoE needs serious hardware or aggressive quants/offload.

What happened to Qwen free API?
Consumer Studio chat stayed free; the developer OAuth free tier was discontinued around 2026-04-15, pushing builders to Coding Plan, paid API, or third parties.

Is DeepSeek safe for proprietary code?
Hosted API = prompts leave your boundary to a China-based service under DeepSeek’s privacy terms. Many orgs require self-host or approved regional hosts. MIT weights make self-host viable if you can operate the stack.

Do rankings flip every month?
Yes. V4, Qwen3.6/3.7, and peer Chinese models leapfrog on public benches. Re-evaluate on your eval set quarterly.

Is Coding Plan still $10 Lite?
Plan carefully: earlier Lite SKUs (~$10 class, later marketing shifts) were discontinued for new subs and renewals in spring 2026. Current docs emphasize Pro-class ~$50/month with 90k requests/month-style ceilings—confirm live console pricing.

Sources

This comparison is backed by 132 primary and secondary sources in research_cache/qwen-vs-deepseek_sources.json: official Qwen/Alibaba and DeepSeek product, pricing, and docs pages; Hugging Face/GitHub open-weight cards; independent 2026 reviews and pricing aggregators; Reddit threads; Hacker News discussions; security and government restriction reporting; and video breakdowns. No inline citation markers in the body—findings are synthesized from that file.

Bottom line

In mid-2026, DeepSeek is still the default answer when someone asks “what’s the cheapest serious text API that isn’t a toy?”—V4 Flash/Pro, brutal cache pricing, MIT weights, simple docs. Qwen is still the default answer when someone asks “which open Chinese family can cover multimodal, local GPUs, and a full product surface?”—Studio, Code, VL/Omni, and a dense open ladder under Apache-style licenses.

If you only integrate one hosted API for pure coding/RAG volume: start with DeepSeek V4 Flash and measure quality gates before paying for Pro. If you only want one ecosystem for apps that see images and might run offline: start with Qwen Plus + open dense fallback. Power users keep both behind a router and treat China-hosted endpoints as non-compliant for regulated data unless legal says otherwise.