Pick DeepSeek R1 for open-weight reasoning model or transparent chain-of-thought. 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, MiniMax M2.7 is the value pick.
DeepSeek R1 (DeepSeek) and MiniMax M2.7 (MiniMax) are two of the models people most often weigh against each other in 2026. DeepSeek R1 is the open-weight reasoning model that reset price expectations in early 2025. 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: MiniMax M2.7 is about 1.8× cheaper on input ($0.3/$1.2 per 1M tokens vs $0.55/$2.19 per 1M tokens) — modest, but it adds up at steady volume.
Context window: MiniMax M2.7 holds 1.6× more — 205K (~307 pages) vs 128K (~192 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 14 months (released March 18, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
DeepSeek R1
MiniMax M2.7
Provider
DeepSeek (China)
MiniMax (China)
Released
January 2025
March 18, 2026
Context window
128K (~192 pages)
205K (~307 pages)
Price (in/out)
$0.55/$2.19 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
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Open-weight reasoning model: DeepSeek R1 — MiniMax M2.7 is comparatively weak here — 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
Transparent chain-of-thought: DeepSeek R1 — DeepSeek R1 lists transparent chain-of-thought among its strengths; MiniMax M2.7 does not.
Low cost: DeepSeek R1 — DeepSeek R1 lists low cost 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 — At $0.3/$1.2 per 1M tokens it undercuts DeepSeek R1 ($0.55/$2.19 per 1M tokens), and that gap compounds at volume.
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 runs cheaper at $0.3/$1.2 per 1M tokens.
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 carries the larger 205K context.
Lowest cost at scale: MiniMax M2.7 — At $0.3/$1.2 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 R1's 128K, fitting roughly 307 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: MiniMax M2.7 — At $0.3/$1.2 per 1M tokens it undercuts DeepSeek R1, 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 open-weight reasoning model: DeepSeek R1 — 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 R1: where it fits
The open-weight reasoning model that reset price expectations in early 2025. Released January 2025 by DeepSeek, it is built for open-weight reasoning model, transparent chain-of-thought, low cost, and strong maths and code.
Its trade-offs are real: older than V4, smaller 128K context, and text/code focused. At $0.55 in / $2.19 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 R1 and MiniMax M2.7 overlap enough that the right pick depends on your specific job. MiniMax M2.7 costs less per token; MiniMax M2.7 holds the larger context; and each leads in its own area — DeepSeek R1 for open-weight reasoning model, 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 R1 or MiniMax M2.7 better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek R1 leans toward open-weight reasoning model 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 R1 or MiniMax M2.7?
MiniMax M2.7 is cheaper — $0.55/$2.19 per 1M tokens vs $0.3/$1.2 per 1M tokens, roughly 1.8× apart on input.
Which has the bigger context window?
MiniMax M2.7 — 205K vs 128K, about 1.6× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek R1 and MiniMax M2.7 together?
Yes — a multi-model platform like LumiChats gives you DeepSeek R1, 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 R1 or MiniMax M2.7?
MiniMax M2.7 — released March 18, 2026, about 14 months after DeepSeek R1.
DeepSeek R1 vs MiniMax M2.7
DeepSeek · China | MiniMax · China · Updated June 2026
Quick verdict
Pick DeepSeek R1 for open-weight reasoning model or transparent chain-of-thought. 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, MiniMax M2.7 is the value pick.
DeepSeek R1 (DeepSeek) and MiniMax M2.7 (MiniMax) are two of the models people most often weigh against each other in 2026. DeepSeek R1 is the open-weight reasoning model that reset price expectations in early 2025. 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: MiniMax M2.7 is about 1.8× cheaper on input ($0.3/$1.2 per 1M tokens vs $0.55/$2.19 per 1M tokens) — modest, but it adds up at steady volume.
▸Context window: MiniMax M2.7 holds 1.6× more — 205K (~307 pages) vs 128K (~192 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 14 months (released March 18, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
DeepSeek R1
MiniMax M2.7
Provider
DeepSeek (China)
MiniMax (China)
Released
January 2025
March 18, 2026
Context window
128K (~192 pages)
205K (~307 pages)
Price (in/out)
$0.55/$2.19 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
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Open-weight reasoning model
DeepSeek R1
MiniMax M2.7 is comparatively weak here — 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
Transparent chain-of-thought
DeepSeek R1
DeepSeek R1 lists transparent chain-of-thought among its strengths; MiniMax M2.7 does not.
Low cost
DeepSeek R1
DeepSeek R1 lists low cost 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
At $0.3/$1.2 per 1M tokens it undercuts DeepSeek R1 ($0.55/$2.19 per 1M tokens), and that gap compounds at volume.
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 runs cheaper at $0.3/$1.2 per 1M tokens.
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 carries the larger 205K context.
Lowest cost at scale
MiniMax M2.7
At $0.3/$1.2 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 R1's 128K, fitting roughly 307 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ MiniMax M2.7
At $0.3/$1.2 per 1M tokens it undercuts DeepSeek R1, 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 open-weight reasoning model
→ DeepSeek R1
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 R1: where it fits
The open-weight reasoning model that reset price expectations in early 2025. Released January 2025 by DeepSeek, it is built for open-weight reasoning model, transparent chain-of-thought, low cost, and strong maths and code.
Its trade-offs are real: older than V4, smaller 128K context, and text/code focused. At $0.55 in / $2.19 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 R1 and MiniMax M2.7 overlap enough that the right pick depends on your specific job. MiniMax M2.7 costs less per token; MiniMax M2.7 holds the larger context; and each leads in its own area — DeepSeek R1 for open-weight reasoning model, 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 R1 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.
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek R1 leans toward open-weight reasoning model 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 R1 or MiniMax M2.7?
MiniMax M2.7 is cheaper — $0.55/$2.19 per 1M tokens vs $0.3/$1.2 per 1M tokens, roughly 1.8× apart on input.
Which has the bigger context window?
MiniMax M2.7 — 205K vs 128K, about 1.6× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek R1 and MiniMax M2.7 together?
Yes — a multi-model platform like LumiChats gives you DeepSeek R1, 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 R1 or MiniMax M2.7?
MiniMax M2.7 — released March 18, 2026, about 14 months after DeepSeek R1.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.