Pick GLM 4.7 for genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions or strong agentic coding for the price — 73.8% on swe-bench verified undercut most closed frontier models 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 4.7 (Z.ai) and MiniMax M3 (MiniMax) are two of the models people most often weigh against each other in 2026. GLM 4.7 is an MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. 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 2× cheaper on input ($0.3/$1.2 per 1M tokens vs $0.6/$2.2 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 (~304 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 5 months (released June 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GLM 4.7
MiniMax M3
Provider
Z.ai (China)
MiniMax (China)
Released
December 22, 2025
June 2026
Context window
200K (~304 pages)
1M (~1,573 pages)
Price (in/out)
$0.6/$2.2 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
73.8%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions: GLM 4.7 — GLM 4.7 lists genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions among its strengths; MiniMax M3 does not.
Strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch: GLM 4.7 — MiniMax M3 is comparatively weak here — price doubles to $0.60/$2.40 above 512K tokens — not flat across 1M
An unusually generous 128K maximum output, which suits bulk refactors and long generation: GLM 4.7 — GLM 4.7 lists an unusually generous 128K maximum output, which suits bulk refactors and long generation 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 4.7'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 — GLM 4.7 is comparatively weak here — its Verified lead narrows sharply on harder evaluations like SWE-Bench Pro
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 4.7'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 4.7, 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 genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions: GLM 4.7 — 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 4.7: where it fits
An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. Released December 22, 2025 by Z.ai, it is built for genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions, strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch, an unusually generous 128K maximum output, which suits bulk refactors and long generation, and cheap long-running agent loops thanks to aggressive prompt caching.
Its trade-offs are real: two generations behind — GLM 5, 5.1 and 5.2 have all shipped since, and new builds should default to those, its Verified lead narrows sharply on harder evaluations like SWE-Bench Pro, and text-only with no vision, and self-hosting a 358B model is a serious hardware commitment. At $0.6 in / $2.2 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 4.7 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 4.7 for genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions, 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 4.7 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 4.7 leans toward genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions 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 4.7 or MiniMax M3?
MiniMax M3 is cheaper — $0.6/$2.2 per 1M tokens vs $0.3/$1.2 per 1M tokens, roughly 2× 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 4.7 and MiniMax M3 together?
Yes — a multi-model platform like LumiChats gives you GLM 4.7, 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 4.7 or MiniMax M3?
MiniMax M3 — released June 2026, about 5 months after GLM 4.7.
GLM 4.7 vs MiniMax M3
Z.ai · China | MiniMax · China · Updated June 2026
Quick verdict
Pick GLM 4.7 for genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions or strong agentic coding for the price — 73.8% on swe-bench verified undercut most closed frontier models 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 4.7 (Z.ai) and MiniMax M3 (MiniMax) are two of the models people most often weigh against each other in 2026. GLM 4.7 is an MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. 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 2× cheaper on input ($0.3/$1.2 per 1M tokens vs $0.6/$2.2 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 (~304 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 5 months (released June 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GLM 4.7
MiniMax M3
Provider
Z.ai (China)
MiniMax (China)
Released
December 22, 2025
June 2026
Context window
200K (~304 pages)
1M (~1,573 pages)
Price (in/out)
$0.6/$2.2 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
73.8%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions
GLM 4.7
GLM 4.7 lists genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions among its strengths; MiniMax M3 does not.
Strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch
GLM 4.7
MiniMax M3 is comparatively weak here — price doubles to $0.60/$2.40 above 512K tokens — not flat across 1M
An unusually generous 128K maximum output, which suits bulk refactors and long generation
GLM 4.7
GLM 4.7 lists an unusually generous 128K maximum output, which suits bulk refactors and long generation 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 4.7'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
GLM 4.7 is comparatively weak here — its Verified lead narrows sharply on harder evaluations like SWE-Bench Pro
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 4.7'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 4.7, 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 genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions
→ GLM 4.7
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 4.7: where it fits
An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. Released December 22, 2025 by Z.ai, it is built for genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions, strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch, an unusually generous 128K maximum output, which suits bulk refactors and long generation, and cheap long-running agent loops thanks to aggressive prompt caching.
Its trade-offs are real: two generations behind — GLM 5, 5.1 and 5.2 have all shipped since, and new builds should default to those, its Verified lead narrows sharply on harder evaluations like SWE-Bench Pro, and text-only with no vision, and self-hosting a 358B model is a serious hardware commitment. At $0.6 in / $2.2 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 4.7 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 4.7 for genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions, 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 4.7 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 4.7 leans toward genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions 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 4.7 or MiniMax M3?
MiniMax M3 is cheaper — $0.6/$2.2 per 1M tokens vs $0.3/$1.2 per 1M tokens, roughly 2× 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 4.7 and MiniMax M3 together?
Yes — a multi-model platform like LumiChats gives you GLM 4.7, 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 4.7 or MiniMax M3?
MiniMax M3 — released June 2026, about 5 months after GLM 4.7.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.