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 MiniMax M2.7 for agentic and terminal coding well above its price tier (57.0 on terminal-bench 2, vendor-reported) or independently ranked 14th of 97 on the artificial analysis intelligence index. On a tight budget at scale, DeepSeek V3.2 is the value pick.
DeepSeek V3.2 (DeepSeek) and MiniMax M2.7 (MiniMax) 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. MiniMax M2.7 is a cheap open-weight agentic coder with near-frontier terminal scores — held back by a non-commercial licence and non-standard benchmarks. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: nearly identical — $0.28/$0.42 per 1M tokens vs $0.3/$1.2 per 1M tokens. Cost will not be the deciding factor here.
Context window: MiniMax M2.7 holds 1.6× more — 205K (~307 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: MiniMax M2.7 is the newer model by about 4 months (released March 18, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
DeepSeek V3.2
MiniMax M2.7
Provider
DeepSeek (China)
MiniMax (China)
Released
December 1, 2025
March 18, 2026
Context window
131K (~197 pages)
205K (~307 pages)
Price (in/out)
$0.28/$0.42 per 1M tokens
$0.3/$1.2 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efficiency via DeepSeek Sparse Attention (DSA): DeepSeek V3.2 — MiniMax M2.7 is comparatively weak here — already superseded internally by M3, and its 205K context is small against 1M-class rivals
Agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes): 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 runs cheaper at $0.28/$0.42 per 1M tokens.
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; MiniMax M2.7 does not.
Agentic and terminal coding well above its price tier (57.0 on Terminal-Bench 2, vendor-reported): MiniMax M2.7 — DeepSeek V3.2 is comparatively weak here — sWE-Bench Verified (73.1) trails the top closed coding models (Claude 4.5 Sonnet 77.2, Gemini 3 Pro 76.2)
Independently ranked 14th of 97 on the Artificial Analysis Intelligence Index: MiniMax M2.7 — A cheap open-weight agentic coder with near-frontier terminal scores — held back by a non-commercial licence and non-standard benchmarks — and it carries the larger 205K context.
Sparse mixture-of-experts — roughly 230B total but only ~10B active, so it runs on local hardware: MiniMax M2.7 — A cheap open-weight agentic coder with near-frontier terminal scores — held back by a non-commercial licence and non-standard benchmarks — and it is the newer of the two.
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: MiniMax M2.7 — Its 205K window is about 1.6× larger than DeepSeek V3.2's 131K, fitting roughly 307 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 MiniMax M2.7, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: MiniMax M2.7 — Larger 205K 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 agentic and terminal coding well above its price tier (57.0 on terminal-bench 2, vendor-reported): MiniMax M2.7 — 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.
MiniMax M2.7: where it fits
A cheap open-weight agentic coder with near-frontier terminal scores — held back by a non-commercial licence and non-standard benchmarks. Released March 18, 2026 by MiniMax, it is built for agentic and terminal coding well above its price tier (57.0 on Terminal-Bench 2, vendor-reported), independently ranked 14th of 97 on the Artificial Analysis Intelligence Index, sparse mixture-of-experts — roughly 230B total but only ~10B active, so it runs on local hardware, and served by five separate hosts at uniform pricing, so there is no provider lock-in.
Its trade-offs: open weights but a NON-COMMERCIAL licence — commercial use requires prior written authorisation from MiniMax, and at least one major tracker still mislabels it as MIT, reports SWE-Bench Pro instead of the standard Verified set, which blocks like-for-like comparison, and already superseded internally by M3, and its 205K context is small against 1M-class rivals. At $0.3 in / $1.2 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
DeepSeek V3.2 and MiniMax M2.7 overlap enough that the right pick depends on your specific job. DeepSeek V3.2 costs less per token; MiniMax M2.7 holds the larger context; and each leads in its own area — DeepSeek V3.2 for long-context efficiency via deepseek sparse attention (dsa), MiniMax M2.7 for agentic and terminal coding well above its price tier (57.0 on terminal-bench 2, vendor-reported). 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 MiniMax M2.7 better for coding?
Public SWE-Bench figures are not available for MiniMax M2.7, 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 MiniMax M2.7 leans toward agentic and terminal coding well above its price tier (57.0 on terminal-bench 2, vendor-reported), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V3.2 or MiniMax M2.7?
DeepSeek V3.2 is cheaper — $0.28/$0.42 per 1M tokens vs $0.3/$1.2 per 1M tokens, roughly 1.1× apart on input.
Which has the bigger context window?
MiniMax M2.7 — 205K vs 131K, about 1.6× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V3.2 and MiniMax M2.7 together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V3.2, MiniMax M2.7 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 MiniMax M2.7?
MiniMax M2.7 — released March 18, 2026, about 4 months after DeepSeek V3.2.
DeepSeek V3.2 vs MiniMax M2.7
DeepSeek · China | MiniMax · 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 MiniMax M2.7 for agentic and terminal coding well above its price tier (57.0 on terminal-bench 2, vendor-reported) or independently ranked 14th of 97 on the artificial analysis intelligence index. On a tight budget at scale, DeepSeek V3.2 is the value pick.
DeepSeek V3.2 (DeepSeek) and MiniMax M2.7 (MiniMax) 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. MiniMax M2.7 is a cheap open-weight agentic coder with near-frontier terminal scores — held back by a non-commercial licence and non-standard benchmarks. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: nearly identical — $0.28/$0.42 per 1M tokens vs $0.3/$1.2 per 1M tokens. Cost will not be the deciding factor here.
▸Context window: MiniMax M2.7 holds 1.6× more — 205K (~307 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: MiniMax M2.7 is the newer model by about 4 months (released March 18, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
DeepSeek V3.2
MiniMax M2.7
Provider
DeepSeek (China)
MiniMax (China)
Released
December 1, 2025
March 18, 2026
Context window
131K (~197 pages)
205K (~307 pages)
Price (in/out)
$0.28/$0.42 per 1M tokens
$0.3/$1.2 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
MiniMax M2.7 is comparatively weak here — already superseded internally by M3, and its 205K context is small against 1M-class rivals
Agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes)
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 runs cheaper at $0.28/$0.42 per 1M tokens.
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; MiniMax M2.7 does not.
Agentic and terminal coding well above its price tier (57.0 on Terminal-Bench 2, vendor-reported)
MiniMax M2.7
DeepSeek V3.2 is comparatively weak here — sWE-Bench Verified (73.1) trails the top closed coding models (Claude 4.5 Sonnet 77.2, Gemini 3 Pro 76.2)
Independently ranked 14th of 97 on the Artificial Analysis Intelligence Index
MiniMax M2.7
A cheap open-weight agentic coder with near-frontier terminal scores — held back by a non-commercial licence and non-standard benchmarks — and it carries the larger 205K context.
Sparse mixture-of-experts — roughly 230B total but only ~10B active, so it runs on local hardware
MiniMax M2.7
A cheap open-weight agentic coder with near-frontier terminal scores — held back by a non-commercial licence and non-standard benchmarks — and it is the newer of the two.
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
MiniMax M2.7
Its 205K window is about 1.6× larger than DeepSeek V3.2's 131K, fitting roughly 307 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 MiniMax M2.7, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ MiniMax M2.7
Larger 205K 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 agentic and terminal coding well above its price tier (57.0 on terminal-bench 2, vendor-reported)
→ MiniMax M2.7
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.
MiniMax M2.7: where it fits
A cheap open-weight agentic coder with near-frontier terminal scores — held back by a non-commercial licence and non-standard benchmarks. Released March 18, 2026 by MiniMax, it is built for agentic and terminal coding well above its price tier (57.0 on Terminal-Bench 2, vendor-reported), independently ranked 14th of 97 on the Artificial Analysis Intelligence Index, sparse mixture-of-experts — roughly 230B total but only ~10B active, so it runs on local hardware, and served by five separate hosts at uniform pricing, so there is no provider lock-in.
Its trade-offs: open weights but a NON-COMMERCIAL licence — commercial use requires prior written authorisation from MiniMax, and at least one major tracker still mislabels it as MIT, reports SWE-Bench Pro instead of the standard Verified set, which blocks like-for-like comparison, and already superseded internally by M3, and its 205K context is small against 1M-class rivals. At $0.3 in / $1.2 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
DeepSeek V3.2 and MiniMax M2.7 overlap enough that the right pick depends on your specific job. DeepSeek V3.2 costs less per token; MiniMax M2.7 holds the larger context; and each leads in its own area — DeepSeek V3.2 for long-context efficiency via deepseek sparse attention (dsa), MiniMax M2.7 for agentic and terminal coding well above its price tier (57.0 on terminal-bench 2, vendor-reported). Rather than crowning one, run the same hard task through both once and let the results decide.
Want both DeepSeek V3.2 and MiniMax M2.7 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 MiniMax M2.7 better for coding?
Public SWE-Bench figures are not available for MiniMax M2.7, 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 MiniMax M2.7 leans toward agentic and terminal coding well above its price tier (57.0 on terminal-bench 2, vendor-reported), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V3.2 or MiniMax M2.7?
DeepSeek V3.2 is cheaper — $0.28/$0.42 per 1M tokens vs $0.3/$1.2 per 1M tokens, roughly 1.1× apart on input.
Which has the bigger context window?
MiniMax M2.7 — 205K vs 131K, about 1.6× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V3.2 and MiniMax M2.7 together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V3.2, MiniMax M2.7 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 MiniMax M2.7?
MiniMax M2.7 — released March 18, 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.