Pick LongCat-2.0 for near-frontier agentic coding — topped openrouter anonymously as 'owl alpha' for two months or massive native 1m context at near-linear cost via sparse attention. Pick MiniMax M3 for open-weight 428b moe (~23b active per token) with a 1m-token context or native multimodal input — text, image and video. On a tight budget at scale, LongCat-2.0 is the value pick.
LongCat-2.0 (Meituan) and MiniMax M3 (MiniMax) are two of the models people most often weigh against each other in 2026. LongCat-2.0 is a trillion-parameter, MIT-licensed open MoE delivering near-frontier agentic coding at 1M context — trained entirely on Chinese chips. MiniMax M3 is miniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Context window: 1M vs 1M — within a few percent of each other, so treat this as a tie and test on your own long inputs, since usable recall varies by model.
Recency: LongCat-2.0 is the newer model by about 34 days (released July 5, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
LongCat-2.0
MiniMax M3
Provider
Meituan (China)
MiniMax (China)
Released
July 5, 2026
June 2026
Context window
1M (~1,500 pages)
1M (~1,573 pages)
Price (in/out)
Open weight (self-host / free)
$0.3/$1.2 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, video, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Near-frontier agentic coding — topped OpenRouter anonymously as 'Owl Alpha' for two months: LongCat-2.0 — A trillion-parameter, MIT-licensed open MoE delivering near-frontier agentic coding at 1M context — trained entirely on Chinese chips — and it is the newer of the two.
Massive native 1M context at near-linear cost via sparse attention: LongCat-2.0 — LongCat-2.0 lists massive native 1M context at near-linear cost via sparse attention among its strengths; MiniMax M3 does not.
Fully MIT-licensed 1.6T-parameter mixture-of-experts (about 48B active): LongCat-2.0 — LongCat-2.0 lists fully MIT-licensed 1.6T-parameter mixture-of-experts (about 48B active) among its strengths; MiniMax M3 does not.
Open-weight 428B MoE (~23B active per token) with a 1M-token context: MiniMax M3 — MiniMax M3 lists open-weight 428B MoE (~23B active per token) with a 1M-token context among its strengths; LongCat-2.0 does not.
Native multimodal input — text, image and video: MiniMax M3 — MiniMax M3 lists native multimodal input — text, image and video among its strengths; LongCat-2.0 does not.
Reports 59.0 on SWE-Bench Pro (a strong open-weight score), surpassing GPT-5.5: MiniMax M3 — LongCat-2.0 is comparatively weak here — headline scores are vendor-reported on SWE-Bench Pro, not the Verified set
Lowest cost at scale: LongCat-2.0 — Its weights are open, so at volume you pay for your own hardware instead of MiniMax M3's $0.3/$1.2 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume: LongCat-2.0 — At Open weight (self-host / free) it undercuts MiniMax M3, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: MiniMax M3 — Larger 1M window fits more in one prompt.
Anyone whose priority is near-frontier agentic coding — topped openrouter anonymously as 'owl alpha' for two months: LongCat-2.0 — It is specifically built for that.
Anyone whose priority is open-weight 428b moe (~23b active per token) with a 1m-token context: MiniMax M3 — That is its strongest area.
LongCat-2.0: where it fits
A trillion-parameter, MIT-licensed open MoE delivering near-frontier agentic coding at 1M context — trained entirely on Chinese chips. Released July 5, 2026 by Meituan, it is built for near-frontier agentic coding — topped OpenRouter anonymously as 'Owl Alpha' for two months, massive native 1M context at near-linear cost via sparse attention, fully MIT-licensed 1.6T-parameter mixture-of-experts (about 48B active), and trained end to end on domestic Chinese chips, independent of Nvidia hardware.
Its trade-offs are real: a 1.6T model is extremely expensive to self-host, so most use leans on the China-hosted API, and headline scores are vendor-reported on SWE-Bench Pro, not the Verified set. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
MiniMax M3: where it fits
MiniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing. Released June 2026 by MiniMax, it is built for open-weight 428B MoE (~23B active per token) with a 1M-token context, native multimodal input — text, image and video, reports 59.0 on SWE-Bench Pro (a strong open-weight score), surpassing GPT-5.5, and low entry pricing at $0.30/$1.20 per million up to 512K tokens.
Its trade-offs: price doubles to $0.60/$2.40 above 512K tokens — not flat across 1M, miniMax's own reported 80.5 SWE-Bench Verified figure is vendor-stated, not independently verified, sWE-Bench Pro is a different, harder benchmark than SWE-Bench Verified, and newer than M2.7 but with less independent testing so far. At $0.3 in / $1.2 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
LongCat-2.0 and MiniMax M3 overlap enough that the right pick depends on your specific job. LongCat-2.0 costs less per token; MiniMax M3 holds the larger context; and each leads in its own area — LongCat-2.0 for near-frontier agentic coding — topped openrouter anonymously as 'owl alpha' for two months, MiniMax M3 for open-weight 428b moe (~23b active per token) with a 1m-token context. Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is LongCat-2.0 or MiniMax M3 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, LongCat-2.0 leans toward near-frontier agentic coding — topped openrouter anonymously as 'owl alpha' for two months while MiniMax M3 leans toward open-weight 428b moe (~23b active per token) with a 1m-token context, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, LongCat-2.0 or MiniMax M3?
LongCat-2.0 is cheaper — Open weight (self-host / free) vs $0.3/$1.2 per 1M tokens.
Which has the bigger context window?
Effectively neither — 1M vs 1M is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both LongCat-2.0 and MiniMax M3 together?
Yes — a multi-model platform like LumiChats gives you LongCat-2.0, MiniMax M3 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, LongCat-2.0 or MiniMax M3?
LongCat-2.0 — released July 5, 2026, about 34 days after MiniMax M3.
LongCat-2.0 vs MiniMax M3
Meituan · China | MiniMax · China · Updated June 2026
Quick verdict
Pick LongCat-2.0 for near-frontier agentic coding — topped openrouter anonymously as 'owl alpha' for two months or massive native 1m context at near-linear cost via sparse attention. Pick MiniMax M3 for open-weight 428b moe (~23b active per token) with a 1m-token context or native multimodal input — text, image and video. On a tight budget at scale, LongCat-2.0 is the value pick.
LongCat-2.0 (Meituan) and MiniMax M3 (MiniMax) are two of the models people most often weigh against each other in 2026. LongCat-2.0 is a trillion-parameter, MIT-licensed open MoE delivering near-frontier agentic coding at 1M context — trained entirely on Chinese chips. MiniMax M3 is miniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Context window: 1M vs 1M — within a few percent of each other, so treat this as a tie and test on your own long inputs, since usable recall varies by model.
▸Recency: LongCat-2.0 is the newer model by about 34 days (released July 5, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
LongCat-2.0
MiniMax M3
Provider
Meituan (China)
MiniMax (China)
Released
July 5, 2026
June 2026
Context window
1M (~1,500 pages)
1M (~1,573 pages)
Price (in/out)
Open weight (self-host / free)
$0.3/$1.2 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, video, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Near-frontier agentic coding — topped OpenRouter anonymously as 'Owl Alpha' for two months
LongCat-2.0
A trillion-parameter, MIT-licensed open MoE delivering near-frontier agentic coding at 1M context — trained entirely on Chinese chips — and it is the newer of the two.
Massive native 1M context at near-linear cost via sparse attention
LongCat-2.0
LongCat-2.0 lists massive native 1M context at near-linear cost via sparse attention among its strengths; MiniMax M3 does not.
LongCat-2.0 lists fully MIT-licensed 1.6T-parameter mixture-of-experts (about 48B active) among its strengths; MiniMax M3 does not.
Open-weight 428B MoE (~23B active per token) with a 1M-token context
MiniMax M3
MiniMax M3 lists open-weight 428B MoE (~23B active per token) with a 1M-token context among its strengths; LongCat-2.0 does not.
Native multimodal input — text, image and video
MiniMax M3
MiniMax M3 lists native multimodal input — text, image and video among its strengths; LongCat-2.0 does not.
Reports 59.0 on SWE-Bench Pro (a strong open-weight score), surpassing GPT-5.5
MiniMax M3
LongCat-2.0 is comparatively weak here — headline scores are vendor-reported on SWE-Bench Pro, not the Verified set
Lowest cost at scale
LongCat-2.0
Its weights are open, so at volume you pay for your own hardware instead of MiniMax M3's $0.3/$1.2 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume
→ LongCat-2.0
At Open weight (self-host / free) it undercuts MiniMax M3, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ MiniMax M3
Larger 1M window fits more in one prompt.
Anyone whose priority is near-frontier agentic coding — topped openrouter anonymously as 'owl alpha' for two months
→ LongCat-2.0
It is specifically built for that.
Anyone whose priority is open-weight 428b moe (~23b active per token) with a 1m-token context
→ MiniMax M3
That is its strongest area.
LongCat-2.0: where it fits
A trillion-parameter, MIT-licensed open MoE delivering near-frontier agentic coding at 1M context — trained entirely on Chinese chips. Released July 5, 2026 by Meituan, it is built for near-frontier agentic coding — topped OpenRouter anonymously as 'Owl Alpha' for two months, massive native 1M context at near-linear cost via sparse attention, fully MIT-licensed 1.6T-parameter mixture-of-experts (about 48B active), and trained end to end on domestic Chinese chips, independent of Nvidia hardware.
Its trade-offs are real: a 1.6T model is extremely expensive to self-host, so most use leans on the China-hosted API, and headline scores are vendor-reported on SWE-Bench Pro, not the Verified set. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
MiniMax M3: where it fits
MiniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing. Released June 2026 by MiniMax, it is built for open-weight 428B MoE (~23B active per token) with a 1M-token context, native multimodal input — text, image and video, reports 59.0 on SWE-Bench Pro (a strong open-weight score), surpassing GPT-5.5, and low entry pricing at $0.30/$1.20 per million up to 512K tokens.
Its trade-offs: price doubles to $0.60/$2.40 above 512K tokens — not flat across 1M, miniMax's own reported 80.5 SWE-Bench Verified figure is vendor-stated, not independently verified, sWE-Bench Pro is a different, harder benchmark than SWE-Bench Verified, and newer than M2.7 but with less independent testing so far. At $0.3 in / $1.2 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
LongCat-2.0 and MiniMax M3 overlap enough that the right pick depends on your specific job. LongCat-2.0 costs less per token; MiniMax M3 holds the larger context; and each leads in its own area — LongCat-2.0 for near-frontier agentic coding — topped openrouter anonymously as 'owl alpha' for two months, MiniMax M3 for open-weight 428b moe (~23b active per token) with a 1m-token context. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both LongCat-2.0 and MiniMax M3 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, LongCat-2.0 leans toward near-frontier agentic coding — topped openrouter anonymously as 'owl alpha' for two months while MiniMax M3 leans toward open-weight 428b moe (~23b active per token) with a 1m-token context, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, LongCat-2.0 or MiniMax M3?
LongCat-2.0 is cheaper — Open weight (self-host / free) vs $0.3/$1.2 per 1M tokens.
Which has the bigger context window?
Effectively neither — 1M vs 1M is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both LongCat-2.0 and MiniMax M3 together?
Yes — a multi-model platform like LumiChats gives you LongCat-2.0, MiniMax M3 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, LongCat-2.0 or MiniMax M3?
LongCat-2.0 — released July 5, 2026, about 34 days after MiniMax M3.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.