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 GLM 5.1 for long-horizon autonomous agentic engineering (up to 8-hour runs) or state-of-the-art open-weight coding (topped swe-bench pro at launch). On a tight budget at scale, DeepSeek V3.2 is the value pick.
DeepSeek V3.2 (DeepSeek) and GLM 5.1 (Z.ai) 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. GLM 5.1 is an open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: DeepSeek V3.2 is about 5× cheaper on input ($0.28/$0.42 per 1M tokens vs $1.4/$4.4 per 1M tokens) — meaningful once you are processing millions of tokens a month.
Context window: GLM 5.1 holds 1.5× more — 200K (~300 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: GLM 5.1 is the newer model by about 4 months (released April 7, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
DeepSeek V3.2
GLM 5.1
Provider
DeepSeek (China)
Z.ai (China)
Released
December 1, 2025
April 7, 2026
Context window
131K (~197 pages)
200K (~300 pages)
Price (in/out)
$0.28/$0.42 per 1M tokens
$1.4/$4.4 per 1M tokens
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 core design strength of DeepSeek V3.2.
Agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes): DeepSeek V3.2 — A core design strength of DeepSeek V3.2.
Elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386): DeepSeek V3.2 — A core design strength of DeepSeek V3.2.
Long-horizon autonomous agentic engineering (up to 8-hour runs): GLM 5.1 — A core design strength of GLM 5.1.
State-of-the-art open-weight coding (topped SWE-Bench Pro at launch): GLM 5.1 — A core design strength of GLM 5.1.
Sustained tool use across thousands of calls: GLM 5.1 — A core design strength of GLM 5.1.
Lowest cost at scale: DeepSeek V3.2 — At $0.28/$0.42 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: GLM 5.1 — Its 200K window is about 1.5× larger, fitting roughly 300 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: DeepSeek V3.2 — At $0.28/$0.42 per 1M tokens it undercuts GLM 5.1, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: GLM 5.1 — Larger 200K 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 long-horizon autonomous agentic engineering (up to 8-hour runs): GLM 5.1 — 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.
GLM 5.1: where it fits
An open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. Released April 7, 2026 by Z.ai, it is built for long-horizon autonomous agentic engineering (up to 8-hour runs), state-of-the-art open-weight coding (topped SWE-Bench Pro at launch), sustained tool use across thousands of calls, and self-hostable under a permissive MIT license.
Its trade-offs: text-only, with no image, audio, or video input, and 754B-parameter MoE demands heavy GPU resources to self-host. At $1.4 in / $4.4 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
DeepSeek V3.2 and GLM 5.1 overlap enough that the right pick depends on your specific job. DeepSeek V3.2 costs less per token; GLM 5.1 holds the larger context; and each leads in its own area — DeepSeek V3.2 for long-context efficiency via deepseek sparse attention (dsa), GLM 5.1 for long-horizon autonomous agentic engineering (up to 8-hour runs). 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 GLM 5.1 better for coding?
Public SWE-Bench figures are not available for GLM 5.1, 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 GLM 5.1 leans toward long-horizon autonomous agentic engineering (up to 8-hour runs), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V3.2 or GLM 5.1?
DeepSeek V3.2 is cheaper — $0.28/$0.42 per 1M tokens vs $1.4/$4.4 per 1M tokens, roughly 5× apart on input.
Which has the bigger context window?
GLM 5.1 — 200K vs 131K, about 1.5× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V3.2 and GLM 5.1 together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V3.2, GLM 5.1 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 GLM 5.1?
GLM 5.1 — released April 7, 2026, about 4 months after DeepSeek V3.2.
DeepSeek V3.2 vs GLM 5.1
DeepSeek · China | Z.ai · 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 GLM 5.1 for long-horizon autonomous agentic engineering (up to 8-hour runs) or state-of-the-art open-weight coding (topped swe-bench pro at launch). On a tight budget at scale, DeepSeek V3.2 is the value pick.
DeepSeek V3.2 (DeepSeek) and GLM 5.1 (Z.ai) 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. GLM 5.1 is an open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: DeepSeek V3.2 is about 5× cheaper on input ($0.28/$0.42 per 1M tokens vs $1.4/$4.4 per 1M tokens) — meaningful once you are processing millions of tokens a month.
▸Context window: GLM 5.1 holds 1.5× more — 200K (~300 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: GLM 5.1 is the newer model by about 4 months (released April 7, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
DeepSeek V3.2
GLM 5.1
Provider
DeepSeek (China)
Z.ai (China)
Released
December 1, 2025
April 7, 2026
Context window
131K (~197 pages)
200K (~300 pages)
Price (in/out)
$0.28/$0.42 per 1M tokens
$1.4/$4.4 per 1M tokens
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 core design strength of DeepSeek V3.2.
Agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes)
DeepSeek V3.2
A core design strength of DeepSeek V3.2.
Elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386)
DeepSeek V3.2
A core design strength of DeepSeek V3.2.
Long-horizon autonomous agentic engineering (up to 8-hour runs)
GLM 5.1
A core design strength of GLM 5.1.
State-of-the-art open-weight coding (topped SWE-Bench Pro at launch)
GLM 5.1
A core design strength of GLM 5.1.
Sustained tool use across thousands of calls
GLM 5.1
A core design strength of GLM 5.1.
Lowest cost at scale
DeepSeek V3.2
At $0.28/$0.42 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
GLM 5.1
Its 200K window is about 1.5× larger, fitting roughly 300 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ DeepSeek V3.2
At $0.28/$0.42 per 1M tokens it undercuts GLM 5.1, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ GLM 5.1
Larger 200K 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 long-horizon autonomous agentic engineering (up to 8-hour runs)
→ GLM 5.1
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.
GLM 5.1: where it fits
An open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. Released April 7, 2026 by Z.ai, it is built for long-horizon autonomous agentic engineering (up to 8-hour runs), state-of-the-art open-weight coding (topped SWE-Bench Pro at launch), sustained tool use across thousands of calls, and self-hostable under a permissive MIT license.
Its trade-offs: text-only, with no image, audio, or video input, and 754B-parameter MoE demands heavy GPU resources to self-host. At $1.4 in / $4.4 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
DeepSeek V3.2 and GLM 5.1 overlap enough that the right pick depends on your specific job. DeepSeek V3.2 costs less per token; GLM 5.1 holds the larger context; and each leads in its own area — DeepSeek V3.2 for long-context efficiency via deepseek sparse attention (dsa), GLM 5.1 for long-horizon autonomous agentic engineering (up to 8-hour runs). Rather than crowning one, run the same hard task through both once and let the results decide.
Want both DeepSeek V3.2 and GLM 5.1 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.
Public SWE-Bench figures are not available for GLM 5.1, 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 GLM 5.1 leans toward long-horizon autonomous agentic engineering (up to 8-hour runs), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V3.2 or GLM 5.1?
DeepSeek V3.2 is cheaper — $0.28/$0.42 per 1M tokens vs $1.4/$4.4 per 1M tokens, roughly 5× apart on input.
Which has the bigger context window?
GLM 5.1 — 200K vs 131K, about 1.5× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V3.2 and GLM 5.1 together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V3.2, GLM 5.1 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 GLM 5.1?
GLM 5.1 — released April 7, 2026, about 4 months after DeepSeek V3.2.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.