Pick Gemini 3.1 Pro for largest mainstream production context (2m) or long video and document analysis. Pick GLM 5 for agentic planning and long-horizon coding workflows or complex systems design and backend reasoning. Choose GLM 5 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 5 (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 5 is z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Price: GLM 5 is about 2× cheaper on input ($1/$3.2 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 10× more — 2M (~3,000 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.
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 5
Provider
Google (US)
Z.ai (China)
Released
February 19, 2026
February 11, 2026
Context window
2M (~3,000 pages)
200K (~300 pages)
Price (in/out)
$2/$12 per 1M tokens
$1/$3.2 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, audio, video, code
text, code
SWE-Bench Verified
Not published
77.8%
MRCR v2 @ 1M
26.3%
Not published
Who wins what
Largest mainstream production context (2M): Gemini 3.1 Pro — A core design strength of Gemini 3.1 Pro.
Long video and document analysis: Gemini 3.1 Pro — A core design strength of Gemini 3.1 Pro.
Agentic reasoning (high ARC-AGI-2): Gemini 3.1 Pro — A core design strength of Gemini 3.1 Pro.
Agentic planning and long-horizon coding workflows: GLM 5 — A core design strength of GLM 5.
Complex systems design and backend reasoning: GLM 5 — A core design strength of GLM 5.
Iterative self-correction on autonomous tasks: GLM 5 — A core design strength of GLM 5.
Lowest cost at scale: GLM 5 — At $1/$3.2 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 10× larger, fitting roughly 3,000 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: GLM 5 — At $1/$3.2 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 5 — 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 agentic planning and long-horizon coding workflows: GLM 5 — That is its strongest area.
An enterprise with regional data-residency rules: Gemini 3.1 Pro or GLM 5 — 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 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: 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.
The bottom line for this matchup
The defining split here is open vs. closed. GLM 5 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 5 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 5 leans toward agentic planning and long-horizon coding workflows, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemini 3.1 Pro or GLM 5?
GLM 5 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 10× 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 5 together?
Yes — a multi-model platform like LumiChats gives you Gemini 3.1 Pro, GLM 5 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 5?
Gemini 3.1 Pro — released February 19, 2026, about 8 days after GLM 5.
Gemini 3.1 Pro vs GLM 5
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 5 for agentic planning and long-horizon coding workflows or complex systems design and backend reasoning. Choose GLM 5 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 5 (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 5 is z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. 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 5 is about 2× cheaper on input ($1/$3.2 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 10× more — 2M (~3,000 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.
▸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 5
Provider
Google (US)
Z.ai (China)
Released
February 19, 2026
February 11, 2026
Context window
2M (~3,000 pages)
200K (~300 pages)
Price (in/out)
$2/$12 per 1M tokens
$1/$3.2 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, audio, video, code
text, code
SWE-Bench Verified
Not published
77.8%
MRCR v2 @ 1M
26.3%
Not published
Who wins what
Largest mainstream production context (2M)
Gemini 3.1 Pro
A core design strength of Gemini 3.1 Pro.
Long video and document analysis
Gemini 3.1 Pro
A core design strength of Gemini 3.1 Pro.
Agentic reasoning (high ARC-AGI-2)
Gemini 3.1 Pro
A core design strength of Gemini 3.1 Pro.
Agentic planning and long-horizon coding workflows
GLM 5
A core design strength of GLM 5.
Complex systems design and backend reasoning
GLM 5
A core design strength of GLM 5.
Iterative self-correction on autonomous tasks
GLM 5
A core design strength of GLM 5.
Lowest cost at scale
GLM 5
At $1/$3.2 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 10× larger, fitting roughly 3,000 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ GLM 5
At $1/$3.2 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 5
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 agentic planning and long-horizon coding workflows
→ GLM 5
That is its strongest area.
An enterprise with regional data-residency rules
→ Gemini 3.1 Pro or GLM 5
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 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: 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.
The bottom line for this matchup
The defining split here is open vs. closed. GLM 5 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 5 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 5 leans toward agentic planning and long-horizon coding workflows, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemini 3.1 Pro or GLM 5?
GLM 5 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 10× 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 5 together?
Yes — a multi-model platform like LumiChats gives you Gemini 3.1 Pro, GLM 5 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 5?
Gemini 3.1 Pro — released February 19, 2026, about 8 days 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.