Pick DeepSeek V3.2 for long-context efficiency via deepseek sparse attention (dsa) or agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes). Pick Qwen3 235B A22B for deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux) or exceptional multilingual and alignment results (79.2 arena-hard v2, 85.2 writingbench). On a tight budget at scale, Qwen3 235B A22B is the value pick.
DeepSeek V3.2 (DeepSeek) and Qwen3 235B A22B (Alibaba) are two of the models people most often weigh against each other in 2026. DeepSeek V3.2 is a cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference. Qwen3 235B A22B is an older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Context window: Qwen3 235B A22B holds 2× more — 256K (~393 pages) vs 131K (~197 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: DeepSeek V3.2 is the newer model by about 4 months (released December 1, 2025), usually meaning fresher training data and capabilities.
Specifications
Spec
DeepSeek V3.2
Qwen3 235B A22B
Provider
DeepSeek (China)
Alibaba (China)
Released
December 1, 2025
July 21, 2025
Context window
131K (~197 pages)
256K (~393 pages)
Price (in/out)
$0.28/$0.42 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
73.1%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Long-context efficiency via DeepSeek Sparse Attention (DSA): DeepSeek V3.2 — A cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference — and it is the newer of the two.
Agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes): DeepSeek V3.2 — Qwen3 235B A22B is comparatively weak here — text-only with no vision, and the absence of a thinking mode caps its hardest reasoning
Elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386): DeepSeek V3.2 — DeepSeek V3.2 lists elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386) among its strengths; Qwen3 235B A22B does not.
Deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux): Qwen3 235B A22B — An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding — and it carries the larger 256K context.
Exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench): Qwen3 235B A22B — Qwen3 235B A22B lists exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench) among its strengths; DeepSeek V3.2 does not.
Outstanding structured logic — 95.0 on ZebraLogic: Qwen3 235B A22B — Qwen3 235B A22B lists outstanding structured logic — 95.0 on ZebraLogic among its strengths; DeepSeek V3.2 does not.
Lowest cost at scale: Qwen3 235B A22B — Its weights are open, so at volume you pay for your own hardware instead of DeepSeek V3.2's $0.28/$0.42 per 1M tokens.
Largest single-prompt input: Qwen3 235B A22B — Its 256K window is about 2× larger than DeepSeek V3.2's 131K, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Qwen3 235B A22B — At Open weight (self-host / free) it undercuts DeepSeek V3.2, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Qwen3 235B A22B — Larger 256K window fits more in one prompt.
Anyone whose priority is long-context efficiency via deepseek sparse attention (dsa): DeepSeek V3.2 — It is specifically built for that.
Anyone whose priority is deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux): Qwen3 235B A22B — That is its strongest area.
DeepSeek V3.2: where it fits
A cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference. Released December 1, 2025 by DeepSeek, it is built for long-context efficiency via DeepSeek Sparse Attention (DSA), agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes), elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386), and low-cost, open-weight (MIT) self-hosting.
Its trade-offs are real: text-only — no image, audio, or video input, and sWE-Bench Verified (73.1) trails the top closed coding models (Claude 4.5 Sonnet 77.2, Gemini 3 Pro 76.2). At $0.28 in / $0.42 out per million tokens, it sits in the budget price band.
Qwen3 235B A22B: where it fits
An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Released July 21, 2025 by Alibaba, it is built for deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux), exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench), outstanding structured logic — 95.0 on ZebraLogic, and no thinking mode, which makes latency and token spend entirely predictable.
Its trade-offs: nearly a year old and superseded — Artificial Analysis now steers users to Qwen3.5-397B instead, text-only with no vision, and the absence of a thinking mode caps its hardest reasoning, coding is weak by 2026 standards, and it publishes no SWE-Bench score to compare on, and its 235B weights need roughly 438GB in BF16, far beyond consumer hardware. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
DeepSeek V3.2 and Qwen3 235B A22B overlap enough that the right pick depends on your specific job. Qwen3 235B A22B costs less per token; Qwen3 235B A22B holds the larger context; and each leads in its own area — DeepSeek V3.2 for long-context efficiency via deepseek sparse attention (dsa), Qwen3 235B A22B for deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux). Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is DeepSeek V3.2 or Qwen3 235B A22B better for coding?
Public SWE-Bench figures are not available for Qwen3 235B A22B, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek V3.2 leans toward long-context efficiency via deepseek sparse attention (dsa) while Qwen3 235B A22B leans toward deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V3.2 or Qwen3 235B A22B?
Qwen3 235B A22B is cheaper — $0.28/$0.42 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Qwen3 235B A22B — 256K vs 131K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V3.2 and Qwen3 235B A22B together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V3.2, Qwen3 235B A22B and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, DeepSeek V3.2 or Qwen3 235B A22B?
DeepSeek V3.2 — released December 1, 2025, about 4 months after Qwen3 235B A22B.
DeepSeek V3.2 vs Qwen3 235B A22B
DeepSeek · China | Alibaba · China · Updated June 2026
Quick verdict
Pick DeepSeek V3.2 for long-context efficiency via deepseek sparse attention (dsa) or agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes). Pick Qwen3 235B A22B for deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux) or exceptional multilingual and alignment results (79.2 arena-hard v2, 85.2 writingbench). On a tight budget at scale, Qwen3 235B A22B is the value pick.
DeepSeek V3.2 (DeepSeek) and Qwen3 235B A22B (Alibaba) are two of the models people most often weigh against each other in 2026. DeepSeek V3.2 is a cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference. Qwen3 235B A22B is an older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Context window: Qwen3 235B A22B holds 2× more — 256K (~393 pages) vs 131K (~197 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: DeepSeek V3.2 is the newer model by about 4 months (released December 1, 2025), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
DeepSeek V3.2
Qwen3 235B A22B
Provider
DeepSeek (China)
Alibaba (China)
Released
December 1, 2025
July 21, 2025
Context window
131K (~197 pages)
256K (~393 pages)
Price (in/out)
$0.28/$0.42 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
73.1%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Long-context efficiency via DeepSeek Sparse Attention (DSA)
DeepSeek V3.2
A cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference — and it is the newer of the two.
Agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes)
DeepSeek V3.2
Qwen3 235B A22B is comparatively weak here — text-only with no vision, and the absence of a thinking mode caps its hardest reasoning
Elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386)
DeepSeek V3.2
DeepSeek V3.2 lists elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386) among its strengths; Qwen3 235B A22B does not.
Deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux)
Qwen3 235B A22B
An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding — and it carries the larger 256K context.
Exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench)
Qwen3 235B A22B
Qwen3 235B A22B lists exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench) among its strengths; DeepSeek V3.2 does not.
Outstanding structured logic — 95.0 on ZebraLogic
Qwen3 235B A22B
Qwen3 235B A22B lists outstanding structured logic — 95.0 on ZebraLogic among its strengths; DeepSeek V3.2 does not.
Lowest cost at scale
Qwen3 235B A22B
Its weights are open, so at volume you pay for your own hardware instead of DeepSeek V3.2's $0.28/$0.42 per 1M tokens.
Largest single-prompt input
Qwen3 235B A22B
Its 256K window is about 2× larger than DeepSeek V3.2's 131K, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Qwen3 235B A22B
At Open weight (self-host / free) it undercuts DeepSeek V3.2, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Qwen3 235B A22B
Larger 256K window fits more in one prompt.
Anyone whose priority is long-context efficiency via deepseek sparse attention (dsa)
→ DeepSeek V3.2
It is specifically built for that.
Anyone whose priority is deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux)
→ Qwen3 235B A22B
That is its strongest area.
DeepSeek V3.2: where it fits
A cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference. Released December 1, 2025 by DeepSeek, it is built for long-context efficiency via DeepSeek Sparse Attention (DSA), agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes), elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386), and low-cost, open-weight (MIT) self-hosting.
Its trade-offs are real: text-only — no image, audio, or video input, and sWE-Bench Verified (73.1) trails the top closed coding models (Claude 4.5 Sonnet 77.2, Gemini 3 Pro 76.2). At $0.28 in / $0.42 out per million tokens, it sits in the budget price band.
Qwen3 235B A22B: where it fits
An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Released July 21, 2025 by Alibaba, it is built for deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux), exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench), outstanding structured logic — 95.0 on ZebraLogic, and no thinking mode, which makes latency and token spend entirely predictable.
Its trade-offs: nearly a year old and superseded — Artificial Analysis now steers users to Qwen3.5-397B instead, text-only with no vision, and the absence of a thinking mode caps its hardest reasoning, coding is weak by 2026 standards, and it publishes no SWE-Bench score to compare on, and its 235B weights need roughly 438GB in BF16, far beyond consumer hardware. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
DeepSeek V3.2 and Qwen3 235B A22B overlap enough that the right pick depends on your specific job. Qwen3 235B A22B costs less per token; Qwen3 235B A22B holds the larger context; and each leads in its own area — DeepSeek V3.2 for long-context efficiency via deepseek sparse attention (dsa), Qwen3 235B A22B for deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux). Rather than crowning one, run the same hard task through both once and let the results decide.
Want both DeepSeek V3.2 and Qwen3 235B A22B without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
Is DeepSeek V3.2 or Qwen3 235B A22B better for coding?
Public SWE-Bench figures are not available for Qwen3 235B A22B, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek V3.2 leans toward long-context efficiency via deepseek sparse attention (dsa) while Qwen3 235B A22B leans toward deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V3.2 or Qwen3 235B A22B?
Qwen3 235B A22B is cheaper — $0.28/$0.42 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Qwen3 235B A22B — 256K vs 131K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V3.2 and Qwen3 235B A22B together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V3.2, Qwen3 235B A22B and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, DeepSeek V3.2 or Qwen3 235B A22B?
DeepSeek V3.2 — released December 1, 2025, about 4 months after Qwen3 235B A22B.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.