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 MiMo-V2.5-Pro for complex software engineering (top-ranked on swe-bench pro) or long-horizon autonomous tasks (1,000+ tool calls). On a tight budget at scale, MiMo-V2.5-Pro is the value pick.
GLM 4.7 (Z.ai) and MiMo-V2.5-Pro (Xiaomi) 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. MiMo-V2.5-Pro is xiaomi's flagship agentic model — autonomous, long-horizon software engineering at a fraction of frontier cost. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: MiMo-V2.5-Pro is about 1.4× cheaper on input ($0.435/$0.87 per 1M tokens vs $0.6/$2.2 per 1M tokens) — modest, but it adds up at steady volume.
Context window: MiMo-V2.5-Pro holds 4.9× more — 1M (~1,500 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: MiMo-V2.5-Pro is the newer model by about 4 months (released April 22, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GLM 4.7
MiMo-V2.5-Pro
Provider
Z.ai (China)
Xiaomi (China)
Released
December 22, 2025
April 22, 2026
Context window
200K (~304 pages)
1M (~1,500 pages)
Price (in/out)
$0.6/$2.2 per 1M tokens
$0.435/$0.87 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; MiMo-V2.5-Pro does not.
Strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch: GLM 4.7 — MiMo-V2.5-Pro is comparatively weak here — benchmark rankings are largely vendor-stated
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; MiMo-V2.5-Pro does not.
Complex software engineering (top-ranked on SWE-bench Pro): MiMo-V2.5-Pro — GLM 4.7 is comparatively weak here — its Verified lead narrows sharply on harder evaluations like SWE-Bench Pro
Long-horizon autonomous tasks (1,000+ tool calls): MiMo-V2.5-Pro — Its 1M window holds about 4.9× more than GLM 4.7's 200K in a single prompt.
Strong on GDPVal and ClawEval: MiMo-V2.5-Pro — Xiaomi's flagship agentic model — autonomous, long-horizon software engineering at a fraction of frontier cost — and it runs cheaper at $0.435/$0.87 per 1M tokens.
Lowest cost at scale: MiMo-V2.5-Pro — At $0.435/$0.87 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: MiMo-V2.5-Pro — Its 1M window is about 4.9× larger than GLM 4.7's 200K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: MiMo-V2.5-Pro — At $0.435/$0.87 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: MiMo-V2.5-Pro — 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 complex software engineering (top-ranked on swe-bench pro): MiMo-V2.5-Pro — 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.
MiMo-V2.5-Pro: where it fits
Xiaomi's flagship agentic model — autonomous, long-horizon software engineering at a fraction of frontier cost. Released April 22, 2026 by Xiaomi, it is built for complex software engineering (top-ranked on SWE-bench Pro), long-horizon autonomous tasks (1,000+ tool calls), strong on GDPVal and ClawEval, and agent-framework integration.
Its trade-offs: benchmark rankings are largely vendor-stated, and limited Western adoption and tooling. At $0.435 in / $0.87 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
GLM 4.7 and MiMo-V2.5-Pro overlap enough that the right pick depends on your specific job. MiMo-V2.5-Pro costs less per token; MiMo-V2.5-Pro 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, MiMo-V2.5-Pro for complex software engineering (top-ranked on swe-bench pro). 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 MiMo-V2.5-Pro better for coding?
Public SWE-Bench figures are not available for MiMo-V2.5-Pro, 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 MiMo-V2.5-Pro leans toward complex software engineering (top-ranked on swe-bench pro), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 4.7 or MiMo-V2.5-Pro?
MiMo-V2.5-Pro is cheaper — $0.6/$2.2 per 1M tokens vs $0.435/$0.87 per 1M tokens, roughly 1.4× apart on input.
Which has the bigger context window?
MiMo-V2.5-Pro — 1M vs 200K, about 4.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 4.7 and MiMo-V2.5-Pro together?
Yes — a multi-model platform like LumiChats gives you GLM 4.7, MiMo-V2.5-Pro 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 MiMo-V2.5-Pro?
MiMo-V2.5-Pro — released April 22, 2026, about 4 months after GLM 4.7.
GLM 4.7 vs MiMo-V2.5-Pro
Z.ai · China | Xiaomi · 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 MiMo-V2.5-Pro for complex software engineering (top-ranked on swe-bench pro) or long-horizon autonomous tasks (1,000+ tool calls). On a tight budget at scale, MiMo-V2.5-Pro is the value pick.
GLM 4.7 (Z.ai) and MiMo-V2.5-Pro (Xiaomi) 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. MiMo-V2.5-Pro is xiaomi's flagship agentic model — autonomous, long-horizon software engineering at a fraction of frontier cost. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: MiMo-V2.5-Pro is about 1.4× cheaper on input ($0.435/$0.87 per 1M tokens vs $0.6/$2.2 per 1M tokens) — modest, but it adds up at steady volume.
▸Context window: MiMo-V2.5-Pro holds 4.9× more — 1M (~1,500 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: MiMo-V2.5-Pro is the newer model by about 4 months (released April 22, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GLM 4.7
MiMo-V2.5-Pro
Provider
Z.ai (China)
Xiaomi (China)
Released
December 22, 2025
April 22, 2026
Context window
200K (~304 pages)
1M (~1,500 pages)
Price (in/out)
$0.6/$2.2 per 1M tokens
$0.435/$0.87 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; MiMo-V2.5-Pro does not.
Strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch
GLM 4.7
MiMo-V2.5-Pro is comparatively weak here — benchmark rankings are largely vendor-stated
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; MiMo-V2.5-Pro does not.
Complex software engineering (top-ranked on SWE-bench Pro)
MiMo-V2.5-Pro
GLM 4.7 is comparatively weak here — its Verified lead narrows sharply on harder evaluations like SWE-Bench Pro
Long-horizon autonomous tasks (1,000+ tool calls)
MiMo-V2.5-Pro
Its 1M window holds about 4.9× more than GLM 4.7's 200K in a single prompt.
Strong on GDPVal and ClawEval
MiMo-V2.5-Pro
Xiaomi's flagship agentic model — autonomous, long-horizon software engineering at a fraction of frontier cost — and it runs cheaper at $0.435/$0.87 per 1M tokens.
Lowest cost at scale
MiMo-V2.5-Pro
At $0.435/$0.87 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
MiMo-V2.5-Pro
Its 1M window is about 4.9× larger than GLM 4.7's 200K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ MiMo-V2.5-Pro
At $0.435/$0.87 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
→ MiMo-V2.5-Pro
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 complex software engineering (top-ranked on swe-bench pro)
→ MiMo-V2.5-Pro
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.
MiMo-V2.5-Pro: where it fits
Xiaomi's flagship agentic model — autonomous, long-horizon software engineering at a fraction of frontier cost. Released April 22, 2026 by Xiaomi, it is built for complex software engineering (top-ranked on SWE-bench Pro), long-horizon autonomous tasks (1,000+ tool calls), strong on GDPVal and ClawEval, and agent-framework integration.
Its trade-offs: benchmark rankings are largely vendor-stated, and limited Western adoption and tooling. At $0.435 in / $0.87 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
GLM 4.7 and MiMo-V2.5-Pro overlap enough that the right pick depends on your specific job. MiMo-V2.5-Pro costs less per token; MiMo-V2.5-Pro 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, MiMo-V2.5-Pro for complex software engineering (top-ranked on swe-bench pro). Rather than crowning one, run the same hard task through both once and let the results decide.
Want both GLM 4.7 and MiMo-V2.5-Pro 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 MiMo-V2.5-Pro, 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 MiMo-V2.5-Pro leans toward complex software engineering (top-ranked on swe-bench pro), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 4.7 or MiMo-V2.5-Pro?
MiMo-V2.5-Pro is cheaper — $0.6/$2.2 per 1M tokens vs $0.435/$0.87 per 1M tokens, roughly 1.4× apart on input.
Which has the bigger context window?
MiMo-V2.5-Pro — 1M vs 200K, about 4.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 4.7 and MiMo-V2.5-Pro together?
Yes — a multi-model platform like LumiChats gives you GLM 4.7, MiMo-V2.5-Pro 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 MiMo-V2.5-Pro?
MiMo-V2.5-Pro — released April 22, 2026, about 4 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.