Pick GLM 5 for agentic planning and long-horizon coding workflows or complex systems design and backend reasoning. 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 (Z.ai) and MiniMax M3 (MiniMax) are two of the models people most often weigh against each other in 2026. GLM 5 is z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. 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 3.3× cheaper on input ($0.3/$1.2 per 1M tokens vs $1/$3.2 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 4 months (released June 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GLM 5
MiniMax M3
Provider
Z.ai (China)
MiniMax (China)
Released
February 11, 2026
June 2026
Context window
200K (~300 pages)
1M (~1,573 pages)
Price (in/out)
$1/$3.2 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
77.8%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Agentic planning and long-horizon coding workflows: GLM 5 — GLM 5 lists agentic planning and long-horizon coding workflows among its strengths; MiniMax M3 does not.
Complex systems design and backend reasoning: GLM 5 — GLM 5 lists complex systems design and backend reasoning among its strengths; MiniMax M3 does not.
Iterative self-correction on autonomous tasks: GLM 5 — GLM 5 lists iterative self-correction on autonomous tasks among its strengths; MiniMax M3 does not.
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's 200K in a single prompt.
Native multimodal input — text, image and video: 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.
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 carries the larger 1M context.
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'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, 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 agentic planning and long-horizon coding workflows: GLM 5 — 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: where it fits
Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. Released February 11, 2026 by Z.ai, it is built for agentic planning and long-horizon coding workflows, complex systems design and backend reasoning, iterative self-correction on autonomous tasks, and open weights under the permissive MIT license.
Its trade-offs are real: 200K context trails 1M-context rivals, and quickly superseded by GLM-5.1 and GLM-5.2. At $1 in / $3.2 out per million tokens, it sits in the budget 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 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 for agentic planning and long-horizon coding workflows, 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 or MiniMax M3 better for coding?
Public SWE-Bench figures are not available for MiniMax M3, so the honest test is your own repository — run an identical real bug through both. By design, GLM 5 leans toward agentic planning and long-horizon coding workflows 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 or MiniMax M3?
MiniMax M3 is cheaper — $1/$3.2 per 1M tokens vs $0.3/$1.2 per 1M tokens, roughly 3.3× 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 and MiniMax M3 together?
Yes — a multi-model platform like LumiChats gives you GLM 5, 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 or MiniMax M3?
MiniMax M3 — released June 2026, about 4 months after GLM 5.
GLM 5 vs MiniMax M3
Z.ai · China | MiniMax · China · Updated June 2026
Quick verdict
Pick GLM 5 for agentic planning and long-horizon coding workflows or complex systems design and backend reasoning. 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 (Z.ai) and MiniMax M3 (MiniMax) are two of the models people most often weigh against each other in 2026. GLM 5 is z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. 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 3.3× cheaper on input ($0.3/$1.2 per 1M tokens vs $1/$3.2 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 4 months (released June 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GLM 5
MiniMax M3
Provider
Z.ai (China)
MiniMax (China)
Released
February 11, 2026
June 2026
Context window
200K (~300 pages)
1M (~1,573 pages)
Price (in/out)
$1/$3.2 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
77.8%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Agentic planning and long-horizon coding workflows
GLM 5
GLM 5 lists agentic planning and long-horizon coding workflows among its strengths; MiniMax M3 does not.
Complex systems design and backend reasoning
GLM 5
GLM 5 lists complex systems design and backend reasoning among its strengths; MiniMax M3 does not.
Iterative self-correction on autonomous tasks
GLM 5
GLM 5 lists iterative self-correction on autonomous tasks among its strengths; MiniMax M3 does not.
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's 200K in a single prompt.
Native multimodal input — text, image and video
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.
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 carries the larger 1M context.
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'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, 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 agentic planning and long-horizon coding workflows
→ GLM 5
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: where it fits
Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. Released February 11, 2026 by Z.ai, it is built for agentic planning and long-horizon coding workflows, complex systems design and backend reasoning, iterative self-correction on autonomous tasks, and open weights under the permissive MIT license.
Its trade-offs are real: 200K context trails 1M-context rivals, and quickly superseded by GLM-5.1 and GLM-5.2. At $1 in / $3.2 out per million tokens, it sits in the budget 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 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 for agentic planning and long-horizon coding workflows, 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 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 MiniMax M3, so the honest test is your own repository — run an identical real bug through both. By design, GLM 5 leans toward agentic planning and long-horizon coding workflows 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 or MiniMax M3?
MiniMax M3 is cheaper — $1/$3.2 per 1M tokens vs $0.3/$1.2 per 1M tokens, roughly 3.3× 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 and MiniMax M3 together?
Yes — a multi-model platform like LumiChats gives you GLM 5, 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 or MiniMax M3?
MiniMax M3 — released June 2026, about 4 months after GLM 5.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.