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). 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, MiniMax M3 is the value pick.
GLM 5.1 (Z.ai) and MiniMax M3 (MiniMax) are two of the models people most often weigh against each other in 2026. 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. 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
Price: MiniMax M3 is about 4.7× cheaper on input ($0.3/$1.2 per 1M tokens vs $1.4/$4.4 per 1M tokens) — meaningful once you are processing millions of tokens a month.
Context window: MiniMax M3 holds 5.2× more — 1M (~1,573 pages) vs 200K (~300 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 M3 is the newer model by about 55 days (released June 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GLM 5.1
MiniMax M3
Provider
Z.ai (China)
MiniMax (China)
Released
April 7, 2026
June 2026
Context window
200K (~300 pages)
1M (~1,573 pages)
Price (in/out)
$1.4/$4.4 per 1M tokens
$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
Long-horizon autonomous agentic engineering (up to 8-hour runs): GLM 5.1 — GLM 5.1 lists long-horizon autonomous agentic engineering (up to 8-hour runs) among its strengths; MiniMax M3 does not.
State-of-the-art open-weight coding (topped SWE-Bench Pro at launch): GLM 5.1 — MiniMax M3 is comparatively weak here — miniMax's own reported 80.5 SWE-Bench Verified figure is vendor-stated, not independently verified
Sustained tool use across thousands of calls: GLM 5.1 — MiniMax M3 is comparatively weak here — price doubles to $0.60/$2.40 above 512K tokens — not flat across 1M
Open-weight 428B MoE (~23B active per token) with a 1M-token context: MiniMax M3 — Its 1M window holds about 5.2× more than GLM 5.1's 200K in a single prompt.
Native multimodal input — text, image and video: MiniMax M3 — GLM 5.1 is comparatively weak here — text-only, with no image, audio, or video input
Reports 59.0 on SWE-Bench Pro (a strong open-weight score), surpassing GPT-5.5: MiniMax M3 — MiniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing — and it runs cheaper at $0.3/$1.2 per 1M tokens.
Lowest cost at scale: MiniMax M3 — 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 M3 — Its 1M window is about 5.2× larger than GLM 5.1's 200K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: MiniMax M3 — At $0.3/$1.2 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: MiniMax M3 — Larger 1M window fits more in one prompt.
Anyone whose priority is long-horizon autonomous agentic engineering (up to 8-hour runs): GLM 5.1 — 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.
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 are real: 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.
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
GLM 5.1 and MiniMax M3 overlap enough that the right pick depends on your specific job. MiniMax M3 costs less per token; MiniMax M3 holds the larger context; and each leads in its own area — GLM 5.1 for long-horizon autonomous agentic engineering (up to 8-hour runs), 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 GLM 5.1 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, GLM 5.1 leans toward long-horizon autonomous agentic engineering (up to 8-hour runs) 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, GLM 5.1 or MiniMax M3?
MiniMax M3 is cheaper — $1.4/$4.4 per 1M tokens vs $0.3/$1.2 per 1M tokens, roughly 4.7× apart on input.
Which has the bigger context window?
MiniMax M3 — 1M vs 200K, about 5.2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5.1 and MiniMax M3 together?
Yes — a multi-model platform like LumiChats gives you GLM 5.1, 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, GLM 5.1 or MiniMax M3?
MiniMax M3 — released June 2026, about 55 days after GLM 5.1.
GLM 5.1 vs MiniMax M3
Z.ai · China | MiniMax · China · Updated June 2026
Quick verdict
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). 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, MiniMax M3 is the value pick.
GLM 5.1 (Z.ai) and MiniMax M3 (MiniMax) are two of the models people most often weigh against each other in 2026. 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. 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
▸Price: MiniMax M3 is about 4.7× cheaper on input ($0.3/$1.2 per 1M tokens vs $1.4/$4.4 per 1M tokens) — meaningful once you are processing millions of tokens a month.
▸Context window: MiniMax M3 holds 5.2× more — 1M (~1,573 pages) vs 200K (~300 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 M3 is the newer model by about 55 days (released June 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GLM 5.1
MiniMax M3
Provider
Z.ai (China)
MiniMax (China)
Released
April 7, 2026
June 2026
Context window
200K (~300 pages)
1M (~1,573 pages)
Price (in/out)
$1.4/$4.4 per 1M tokens
$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
Long-horizon autonomous agentic engineering (up to 8-hour runs)
GLM 5.1
GLM 5.1 lists long-horizon autonomous agentic engineering (up to 8-hour runs) among its strengths; MiniMax M3 does not.
State-of-the-art open-weight coding (topped SWE-Bench Pro at launch)
GLM 5.1
MiniMax M3 is comparatively weak here — miniMax's own reported 80.5 SWE-Bench Verified figure is vendor-stated, not independently verified
Sustained tool use across thousands of calls
GLM 5.1
MiniMax M3 is comparatively weak here — price doubles to $0.60/$2.40 above 512K tokens — not flat across 1M
Open-weight 428B MoE (~23B active per token) with a 1M-token context
MiniMax M3
Its 1M window holds about 5.2× more than GLM 5.1's 200K in a single prompt.
Native multimodal input — text, image and video
MiniMax M3
GLM 5.1 is comparatively weak here — text-only, with no image, audio, or video input
Reports 59.0 on SWE-Bench Pro (a strong open-weight score), surpassing GPT-5.5
MiniMax M3
MiniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing — and it runs cheaper at $0.3/$1.2 per 1M tokens.
Lowest cost at scale
MiniMax M3
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 M3
Its 1M window is about 5.2× larger than GLM 5.1's 200K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ MiniMax M3
At $0.3/$1.2 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
→ MiniMax M3
Larger 1M window fits more in one prompt.
Anyone whose priority is long-horizon autonomous agentic engineering (up to 8-hour runs)
→ GLM 5.1
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.
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 are real: 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.
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
GLM 5.1 and MiniMax M3 overlap enough that the right pick depends on your specific job. MiniMax M3 costs less per token; MiniMax M3 holds the larger context; and each leads in its own area — GLM 5.1 for long-horizon autonomous agentic engineering (up to 8-hour runs), 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 GLM 5.1 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, GLM 5.1 leans toward long-horizon autonomous agentic engineering (up to 8-hour runs) 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, GLM 5.1 or MiniMax M3?
MiniMax M3 is cheaper — $1.4/$4.4 per 1M tokens vs $0.3/$1.2 per 1M tokens, roughly 4.7× apart on input.
Which has the bigger context window?
MiniMax M3 — 1M vs 200K, about 5.2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5.1 and MiniMax M3 together?
Yes — a multi-model platform like LumiChats gives you GLM 5.1, 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, GLM 5.1 or MiniMax M3?
MiniMax M3 — released June 2026, about 55 days after GLM 5.1.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.