Pick Gemini 3.5 Flash-Lite for the cheapest gemini tier at $0.30/$2.50 — built for the highest-volume, lowest-margin work or simple classification, extraction and routing where flagship reasoning is wasted spend. 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 3.5 Flash-Lite if you want a managed API.
Gemini 3.5 Flash-Lite (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 3.5 Flash-Lite is google's cheapest July 2026 model at $0.30/$2.50 — a high-volume, low-cost Lite tier for simple work, not frontier reasoning. 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: Gemini 3.5 Flash-Lite is about 2× cheaper on input ($0.3/$2.5 per 1M tokens vs $0.6/$2.2 per 1M tokens) — meaningful once you are processing millions of tokens a month.
Context window: Gemini 3.5 Flash-Lite 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: Gemini 3.5 Flash-Lite is the newer model by about 7 months (released July 21, 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 3.5 Flash-Lite
GLM 4.7
Provider
Google (US)
Z.ai (China)
Released
July 21, 2026
December 22, 2025
Context window
1M (~1,500 pages)
200K (~304 pages)
Price (in/out)
$0.3/$2.5 per 1M tokens
$0.6/$2.2 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, code
text, code
SWE-Bench Verified
Not published
73.8%
MRCR v2 @ 1M
Not published
Not published
Who wins what
The cheapest Gemini tier at $0.30/$2.50 — built for the highest-volume, lowest-margin work: Gemini 3.5 Flash-Lite — At $0.3/$2.5 per 1M tokens it undercuts GLM 4.7 ($0.6/$2.2 per 1M tokens), and that gap compounds at volume.
Simple classification, extraction and routing where flagship reasoning is wasted spend: Gemini 3.5 Flash-Lite — Google's cheapest July 2026 model at $0.30/$2.50 — a high-volume, low-cost Lite tier for simple work, not frontier reasoning — and it runs cheaper at $0.3/$2.5 per 1M tokens.
Latency-sensitive pipelines that call a model on every request: Gemini 3.5 Flash-Lite — GLM 4.7 is comparatively weak here — text-only with no vision, and self-hosting a 358B model is a serious hardware commitment
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 3.5 Flash-Lite 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 — Gemini 3.5 Flash-Lite is comparatively weak here — a Lite model — the weakest of the July 2026 Gemini line on hard reasoning and coding
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 its weights are open while Gemini 3.5 Flash-Lite is API-only.
Lowest cost at scale: Gemini 3.5 Flash-Lite — At $0.3/$2.5 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Gemini 3.5 Flash-Lite — 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: Gemini 3.5 Flash-Lite — At $0.3/$2.5 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: Gemini 3.5 Flash-Lite — Larger 1M 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 3.5 Flash-Lite is API-only.
Anyone whose priority is the cheapest gemini tier at $0.30/$2.50 — built for the highest-volume, lowest-margin work: Gemini 3.5 Flash-Lite — 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 3.5 Flash-Lite 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 3.5 Flash-Lite: where it fits
Google's cheapest July 2026 model at $0.30/$2.50 — a high-volume, low-cost Lite tier for simple work, not frontier reasoning. Released July 21, 2026 by Google, it is built for the cheapest Gemini tier at $0.30/$2.50 — built for the highest-volume, lowest-margin work, simple classification, extraction and routing where flagship reasoning is wasted spend, latency-sensitive pipelines that call a model on every request, and pairing with a stronger model as the cheap first pass in a cascade.
Its trade-offs are real: a Lite model — the weakest of the July 2026 Gemini line on hard reasoning and coding, google did not publish its exact context window, so 1M is inferred from the family, no published SWE-Bench Verified score, and outclassed by 3.6 Flash whenever a task needs real capability rather than raw throughput. At $0.3 in / $2.5 out per million tokens, it sits in the budget 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 3.5 Flash-Lite 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 3.5 Flash-Lite or GLM 4.7 better for coding?
Public SWE-Bench figures are not available for Gemini 3.5 Flash-Lite, so the honest test is your own repository — run an identical real bug through both. By design, Gemini 3.5 Flash-Lite leans toward the cheapest gemini tier at $0.30/$2.50 — built for the highest-volume, lowest-margin work 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 3.5 Flash-Lite 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 3.5 Flash-Lite is API-metered at $0.3/$2.5 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 3.5 Flash-Lite — 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 Gemini 3.5 Flash-Lite and GLM 4.7 together?
Yes — a multi-model platform like LumiChats gives you Gemini 3.5 Flash-Lite, 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 3.5 Flash-Lite or GLM 4.7?
Gemini 3.5 Flash-Lite — released July 21, 2026, about 7 months after GLM 4.7.
Gemini 3.5 Flash-Lite vs GLM 4.7
Google · US | Z.ai · China · Updated June 2026
Quick verdict
Pick Gemini 3.5 Flash-Lite for the cheapest gemini tier at $0.30/$2.50 — built for the highest-volume, lowest-margin work or simple classification, extraction and routing where flagship reasoning is wasted spend. 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 3.5 Flash-Lite if you want a managed API.
Gemini 3.5 Flash-Lite (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 3.5 Flash-Lite is google's cheapest July 2026 model at $0.30/$2.50 — a high-volume, low-cost Lite tier for simple work, not frontier reasoning. 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: Gemini 3.5 Flash-Lite is about 2× cheaper on input ($0.3/$2.5 per 1M tokens vs $0.6/$2.2 per 1M tokens) — meaningful once you are processing millions of tokens a month.
▸Context window: Gemini 3.5 Flash-Lite 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: Gemini 3.5 Flash-Lite is the newer model by about 7 months (released July 21, 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 3.5 Flash-Lite
GLM 4.7
Provider
Google (US)
Z.ai (China)
Released
July 21, 2026
December 22, 2025
Context window
1M (~1,500 pages)
200K (~304 pages)
Price (in/out)
$0.3/$2.5 per 1M tokens
$0.6/$2.2 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, code
text, code
SWE-Bench Verified
Not published
73.8%
MRCR v2 @ 1M
Not published
Not published
Who wins what
The cheapest Gemini tier at $0.30/$2.50 — built for the highest-volume, lowest-margin work
Gemini 3.5 Flash-Lite
At $0.3/$2.5 per 1M tokens it undercuts GLM 4.7 ($0.6/$2.2 per 1M tokens), and that gap compounds at volume.
Simple classification, extraction and routing where flagship reasoning is wasted spend
Gemini 3.5 Flash-Lite
Google's cheapest July 2026 model at $0.30/$2.50 — a high-volume, low-cost Lite tier for simple work, not frontier reasoning — and it runs cheaper at $0.3/$2.5 per 1M tokens.
Latency-sensitive pipelines that call a model on every request
Gemini 3.5 Flash-Lite
GLM 4.7 is comparatively weak here — text-only with no vision, and self-hosting a 358B model is a serious hardware commitment
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 3.5 Flash-Lite 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
Gemini 3.5 Flash-Lite is comparatively weak here — a Lite model — the weakest of the July 2026 Gemini line on hard reasoning and coding
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 its weights are open while Gemini 3.5 Flash-Lite is API-only.
Lowest cost at scale
Gemini 3.5 Flash-Lite
At $0.3/$2.5 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Gemini 3.5 Flash-Lite
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
→ Gemini 3.5 Flash-Lite
At $0.3/$2.5 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
→ Gemini 3.5 Flash-Lite
Larger 1M 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 3.5 Flash-Lite is API-only.
Anyone whose priority is the cheapest gemini tier at $0.30/$2.50 — built for the highest-volume, lowest-margin work
→ Gemini 3.5 Flash-Lite
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 3.5 Flash-Lite 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 3.5 Flash-Lite: where it fits
Google's cheapest July 2026 model at $0.30/$2.50 — a high-volume, low-cost Lite tier for simple work, not frontier reasoning. Released July 21, 2026 by Google, it is built for the cheapest Gemini tier at $0.30/$2.50 — built for the highest-volume, lowest-margin work, simple classification, extraction and routing where flagship reasoning is wasted spend, latency-sensitive pipelines that call a model on every request, and pairing with a stronger model as the cheap first pass in a cascade.
Its trade-offs are real: a Lite model — the weakest of the July 2026 Gemini line on hard reasoning and coding, google did not publish its exact context window, so 1M is inferred from the family, no published SWE-Bench Verified score, and outclassed by 3.6 Flash whenever a task needs real capability rather than raw throughput. At $0.3 in / $2.5 out per million tokens, it sits in the budget 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 3.5 Flash-Lite 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 3.5 Flash-Lite 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.
Is Gemini 3.5 Flash-Lite or GLM 4.7 better for coding?
Public SWE-Bench figures are not available for Gemini 3.5 Flash-Lite, so the honest test is your own repository — run an identical real bug through both. By design, Gemini 3.5 Flash-Lite leans toward the cheapest gemini tier at $0.30/$2.50 — built for the highest-volume, lowest-margin work 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 3.5 Flash-Lite 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 3.5 Flash-Lite is API-metered at $0.3/$2.5 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 3.5 Flash-Lite — 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 Gemini 3.5 Flash-Lite and GLM 4.7 together?
Yes — a multi-model platform like LumiChats gives you Gemini 3.5 Flash-Lite, 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 3.5 Flash-Lite or GLM 4.7?
Gemini 3.5 Flash-Lite — released July 21, 2026, about 7 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.