Pick Gemini 3.1 Pro for largest mainstream production context (2m) or long video and document analysis. 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. Choose GLM 4.7 if you need self-hosting or data privacy; Gemini 3.1 Pro if you want a managed API.
Gemini 3.1 Pro (Google, US) and GLM 4.7 (Z.ai, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Gemini 3.1 Pro is a 2M-token multimodal workhorse — huge breadth, but recall fades deep in the window. 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. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Price: GLM 4.7 is about 3.3× cheaper on input ($0.6/$2.2 per 1M tokens vs $2/$12 per 1M tokens) — meaningful once you are processing millions of tokens a month.
Context window: Gemini 3.1 Pro holds 9.9× more — 2M (~3,000 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: Gemini 3.1 Pro is the newer model by about 59 days (released February 19, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
Gemini 3.1 Pro
GLM 4.7
Provider
Google (US)
Z.ai (China)
Released
February 19, 2026
December 22, 2025
Context window
2M (~3,000 pages)
200K (~304 pages)
Price (in/out)
$2/$12 per 1M tokens
$0.6/$2.2 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, audio, video, code
text, code
SWE-Bench Verified
Not published
73.8%
MRCR v2 @ 1M
26.3%
Not published
Who wins what
Largest mainstream production context (2M): Gemini 3.1 Pro — Its 2M window holds about 9.9× more than GLM 4.7's 200K in a single prompt.
Long video and document analysis: Gemini 3.1 Pro — A 2M-token multimodal workhorse — huge breadth, but recall fades deep in the window — and it carries the larger 2M context.
Agentic reasoning (high ARC-AGI-2): Gemini 3.1 Pro — A 2M-token multimodal workhorse — huge breadth, but recall fades deep in the window — and it is the newer of the two.
Genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions: GLM 4.7 — Open weights make this possible at all — Gemini 3.1 Pro is API-only, so it cannot leave the vendor's servers.
Strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch: GLM 4.7 — At $0.6/$2.2 per 1M tokens it undercuts Gemini 3.1 Pro ($2/$12 per 1M tokens), and that gap compounds at volume.
An unusually generous 128K maximum output, which suits bulk refactors and long generation: GLM 4.7 — An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2 — and it runs cheaper at $0.6/$2.2 per 1M tokens.
Lowest cost at scale: GLM 4.7 — At $0.6/$2.2 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Gemini 3.1 Pro — Its 2M window is about 9.9× larger than GLM 4.7's 200K, fitting roughly 3,000 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: GLM 4.7 — At $0.6/$2.2 per 1M tokens it undercuts Gemini 3.1 Pro, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Gemini 3.1 Pro — Larger 2M window fits more in one prompt.
A team with data-privacy or self-hosting needs: GLM 4.7 — Open weights let you run it on your own hardware; Gemini 3.1 Pro is API-only.
Anyone whose priority is largest mainstream production context (2m): Gemini 3.1 Pro — It is specifically built for that.
Anyone whose priority is genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions: GLM 4.7 — That is its strongest area.
An enterprise with regional data-residency rules: Gemini 3.1 Pro or GLM 4.7 — Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Gemini 3.1 Pro: where it fits
A 2M-token multimodal workhorse — huge breadth, but recall fades deep in the window. Released February 19, 2026 by Google, it is built for largest mainstream production context (2M), long video and document analysis, agentic reasoning (high ARC-AGI-2), and multimodal understanding.
Its trade-offs are real: long-context recall drops sharply past 256K, and premium price per token. At $2 in / $12 out per million tokens, it sits in the mid price band.
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: 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.
The bottom line for this matchup
The defining split here is open vs. closed. GLM 4.7 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Gemini 3.1 Pro gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Frequently asked questions
Is Gemini 3.1 Pro or GLM 4.7 better for coding?
Public SWE-Bench figures are not available for Gemini 3.1 Pro, so the honest test is your own repository — run an identical real bug through both. By design, Gemini 3.1 Pro leans toward largest mainstream production context (2m) while GLM 4.7 leans toward genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemini 3.1 Pro or GLM 4.7?
GLM 4.7 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Gemini 3.1 Pro is API-metered at $2/$12 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Gemini 3.1 Pro — 2M vs 200K, about 9.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Gemini 3.1 Pro and GLM 4.7 together?
Yes — a multi-model platform like LumiChats gives you Gemini 3.1 Pro, GLM 4.7 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, Gemini 3.1 Pro or GLM 4.7?
Gemini 3.1 Pro — released February 19, 2026, about 59 days after GLM 4.7.
Gemini 3.1 Pro vs GLM 4.7
Google · US | Z.ai · China · Updated June 2026
Quick verdict
Pick Gemini 3.1 Pro for largest mainstream production context (2m) or long video and document analysis. 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. Choose GLM 4.7 if you need self-hosting or data privacy; Gemini 3.1 Pro if you want a managed API.
Gemini 3.1 Pro (Google, US) and GLM 4.7 (Z.ai, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Gemini 3.1 Pro is a 2M-token multimodal workhorse — huge breadth, but recall fades deep in the window. 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. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Price: GLM 4.7 is about 3.3× cheaper on input ($0.6/$2.2 per 1M tokens vs $2/$12 per 1M tokens) — meaningful once you are processing millions of tokens a month.
▸Context window: Gemini 3.1 Pro holds 9.9× more — 2M (~3,000 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: Gemini 3.1 Pro is the newer model by about 59 days (released February 19, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
Gemini 3.1 Pro
GLM 4.7
Provider
Google (US)
Z.ai (China)
Released
February 19, 2026
December 22, 2025
Context window
2M (~3,000 pages)
200K (~304 pages)
Price (in/out)
$2/$12 per 1M tokens
$0.6/$2.2 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, audio, video, code
text, code
SWE-Bench Verified
Not published
73.8%
MRCR v2 @ 1M
26.3%
Not published
Who wins what
Largest mainstream production context (2M)
Gemini 3.1 Pro
Its 2M window holds about 9.9× more than GLM 4.7's 200K in a single prompt.
Long video and document analysis
Gemini 3.1 Pro
A 2M-token multimodal workhorse — huge breadth, but recall fades deep in the window — and it carries the larger 2M context.
Agentic reasoning (high ARC-AGI-2)
Gemini 3.1 Pro
A 2M-token multimodal workhorse — huge breadth, but recall fades deep in the window — and it is the newer of the two.
Genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions
GLM 4.7
Open weights make this possible at all — Gemini 3.1 Pro is API-only, so it cannot leave the vendor's servers.
Strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch
GLM 4.7
At $0.6/$2.2 per 1M tokens it undercuts Gemini 3.1 Pro ($2/$12 per 1M tokens), and that gap compounds at volume.
An unusually generous 128K maximum output, which suits bulk refactors and long generation
GLM 4.7
An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2 — and it runs cheaper at $0.6/$2.2 per 1M tokens.
Lowest cost at scale
GLM 4.7
At $0.6/$2.2 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Gemini 3.1 Pro
Its 2M window is about 9.9× larger than GLM 4.7's 200K, fitting roughly 3,000 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ GLM 4.7
At $0.6/$2.2 per 1M tokens it undercuts Gemini 3.1 Pro, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Gemini 3.1 Pro
Larger 2M window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ GLM 4.7
Open weights let you run it on your own hardware; Gemini 3.1 Pro is API-only.
Anyone whose priority is largest mainstream production context (2m)
→ Gemini 3.1 Pro
It is specifically built for that.
Anyone whose priority is genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions
→ GLM 4.7
That is its strongest area.
An enterprise with regional data-residency rules
→ Gemini 3.1 Pro or GLM 4.7
Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Gemini 3.1 Pro: where it fits
A 2M-token multimodal workhorse — huge breadth, but recall fades deep in the window. Released February 19, 2026 by Google, it is built for largest mainstream production context (2M), long video and document analysis, agentic reasoning (high ARC-AGI-2), and multimodal understanding.
Its trade-offs are real: long-context recall drops sharply past 256K, and premium price per token. At $2 in / $12 out per million tokens, it sits in the mid price band.
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: 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.
The bottom line for this matchup
The defining split here is open vs. closed. GLM 4.7 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Gemini 3.1 Pro gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Want both Gemini 3.1 Pro and GLM 4.7 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 Gemini 3.1 Pro, so the honest test is your own repository — run an identical real bug through both. By design, Gemini 3.1 Pro leans toward largest mainstream production context (2m) while GLM 4.7 leans toward genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemini 3.1 Pro or GLM 4.7?
GLM 4.7 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Gemini 3.1 Pro is API-metered at $2/$12 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Gemini 3.1 Pro — 2M vs 200K, about 9.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Gemini 3.1 Pro and GLM 4.7 together?
Yes — a multi-model platform like LumiChats gives you Gemini 3.1 Pro, GLM 4.7 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, Gemini 3.1 Pro or GLM 4.7?
Gemini 3.1 Pro — released February 19, 2026, about 59 days 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.