Pick Gemini 3.1 Flash Lite for ultra-low-latency, high-volume production workloads or most cost-efficient gemini 3 model — half the price of gemini 3 flash ($0.25/$1.50 vs $0.50/$3.00 per 1m tokens). Pick Inkling for the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai or a 975b-parameter moe (41b active) with native text, image, and audio reasoning in one model. Choose Inkling if you need self-hosting or data privacy; Gemini 3.1 Flash Lite if you want a managed API.
Gemini 3.1 Flash Lite (Google) and Inkling (Thinking Machines Lab) are two of the models people most often weigh against each other in 2026. Gemini 3.1 Flash Lite is google's fastest and most cost-efficient Gemini 3 series model, built for ultra-low-latency, high-volume production workloads at half the price of Gemini 3 Flash. Inkling is mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control. They diverge most on price and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Cost model: Inkling ships open weights you can self-host (hardware cost only, no per-token fee), while Gemini 3.1 Flash Lite is API-metered at $0.25/$1.5 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
Context window: both advertise 1M (~1,500 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
Recency: Inkling is the newer model by about 4 months (released July 15, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Gemini 3.1 Flash Lite
Inkling
Provider
Google (US)
Thinking Machines Lab (US)
Released
March 3, 2026
July 15, 2026
Context window
1M (~1,500 pages)
1M (~1,500 pages)
Price (in/out)
$0.25/$1.5 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, audio, video
text, image, audio, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
12.3%
Not published
Who wins what
Ultra-low-latency, high-volume production workloads: Gemini 3.1 Flash Lite — Gemini 3.1 Flash Lite lists ultra-low-latency, high-volume production workloads among its strengths; Inkling does not.
Most cost-efficient Gemini 3 model — half the price of Gemini 3 Flash ($0.25/$1.50 vs $0.50/$3.00 per 1M tokens): Gemini 3.1 Flash Lite — Inkling is comparatively weak here — no official hosted API price - available via third-party hosts only at launch
High-volume agentic and tool-calling loops where cost per call matters: Gemini 3.1 Flash Lite — Gemini 3.1 Flash Lite lists high-volume agentic and tool-calling loops where cost per call matters among its strengths; Inkling does not.
The first model release from Mira Murati's Thinking Machines Lab (ex-OpenAI CTO) - one of the most closely-watched new labs in AI: Inkling — Open weights make this possible at all — Gemini 3.1 Flash Lite is API-only, so it cannot leave the vendor's servers.
A 975B-parameter MoE (41B active) with native text, image, and audio reasoning in one model: Inkling — Gemini 3.1 Flash Lite is comparatively weak here — lower reasoning and quality ceiling than Gemini 3.1 Pro and the full Gemini 3 Flash tier
A dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request: Inkling — Mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control — and its weights are open while Gemini 3.1 Flash Lite is API-only.
Lowest cost at scale: Inkling — Its weights are open, so at volume you pay for your own hardware instead of Gemini 3.1 Flash Lite's $0.25/$1.5 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume: Inkling — At Open weight (self-host / free) it undercuts Gemini 3.1 Flash Lite, and on millions of tokens that margin decides the monthly bill.
A team with data-privacy or self-hosting needs: Inkling — Open weights let you run it on your own hardware; Gemini 3.1 Flash Lite is API-only.
Anyone whose priority is ultra-low-latency, high-volume production workloads: Gemini 3.1 Flash Lite — It is specifically built for that.
Anyone whose priority is the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai: Inkling — That is its strongest area.
Gemini 3.1 Flash Lite: where it fits
Google's fastest and most cost-efficient Gemini 3 series model, built for ultra-low-latency, high-volume production workloads at half the price of Gemini 3 Flash. Released March 3, 2026 by Google, it is built for ultra-low-latency, high-volume production workloads, most cost-efficient Gemini 3 model — half the price of Gemini 3 Flash ($0.25/$1.50 vs $0.50/$3.00 per 1M tokens), high-volume agentic and tool-calling loops where cost per call matters, and multimodal input across text, image, video, audio, and PDF.
Its trade-offs are real: lower reasoning and quality ceiling than Gemini 3.1 Pro and the full Gemini 3 Flash tier, sharp long-context degradation — MRCR v2 (8-needle) retrieval falls to ~12% at the full 1M-token window, and closed weights — not downloadable or self-hostable. At $0.25 in / $1.5 out per million tokens, it sits in the budget price band.
Inkling: where it fits
Mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control. Released July 15, 2026 by Thinking Machines Lab, it is built for the first model release from Mira Murati's Thinking Machines Lab (ex-OpenAI CTO) - one of the most closely-watched new labs in AI, a 975B-parameter MoE (41B active) with native text, image, and audio reasoning in one model, a dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request, and fully open weights (Apache 2.0) at frontier scale - unusual for a model this large and this new.
Its trade-offs: a brand-new lab's first release - no multi-generation track record yet, no official hosted API price - available via third-party hosts only at launch, and independent third-party benchmark verification is still limited given how recently it shipped. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
The defining split here is open vs. closed. Inkling gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Gemini 3.1 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 3.1 Flash Lite or Inkling better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Gemini 3.1 Flash Lite leans toward ultra-low-latency, high-volume production workloads while Inkling leans toward the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemini 3.1 Flash Lite or Inkling?
Inkling is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Gemini 3.1 Flash Lite is API-metered at $0.25/$1.5 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?
Both advertise 1M (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Gemini 3.1 Flash Lite and Inkling together?
Yes — a multi-model platform like LumiChats gives you Gemini 3.1 Flash Lite, Inkling 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 Flash Lite or Inkling?
Inkling — released July 15, 2026, about 4 months after Gemini 3.1 Flash Lite.
Gemini 3.1 Flash Lite vs Inkling
Google · US | Thinking Machines Lab · US · Updated June 2026
Quick verdict
Pick Gemini 3.1 Flash Lite for ultra-low-latency, high-volume production workloads or most cost-efficient gemini 3 model — half the price of gemini 3 flash ($0.25/$1.50 vs $0.50/$3.00 per 1m tokens). Pick Inkling for the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai or a 975b-parameter moe (41b active) with native text, image, and audio reasoning in one model. Choose Inkling if you need self-hosting or data privacy; Gemini 3.1 Flash Lite if you want a managed API.
Gemini 3.1 Flash Lite (Google) and Inkling (Thinking Machines Lab) are two of the models people most often weigh against each other in 2026. Gemini 3.1 Flash Lite is google's fastest and most cost-efficient Gemini 3 series model, built for ultra-low-latency, high-volume production workloads at half the price of Gemini 3 Flash. Inkling is mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control. They diverge most on price and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Cost model: Inkling ships open weights you can self-host (hardware cost only, no per-token fee), while Gemini 3.1 Flash Lite is API-metered at $0.25/$1.5 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
▸Context window: both advertise 1M (~1,500 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
▸Recency: Inkling is the newer model by about 4 months (released July 15, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Gemini 3.1 Flash Lite
Inkling
Provider
Google (US)
Thinking Machines Lab (US)
Released
March 3, 2026
July 15, 2026
Context window
1M (~1,500 pages)
1M (~1,500 pages)
Price (in/out)
$0.25/$1.5 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, audio, video
text, image, audio, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
12.3%
Not published
Who wins what
Ultra-low-latency, high-volume production workloads
Gemini 3.1 Flash Lite
Gemini 3.1 Flash Lite lists ultra-low-latency, high-volume production workloads among its strengths; Inkling does not.
Most cost-efficient Gemini 3 model — half the price of Gemini 3 Flash ($0.25/$1.50 vs $0.50/$3.00 per 1M tokens)
Gemini 3.1 Flash Lite
Inkling is comparatively weak here — no official hosted API price - available via third-party hosts only at launch
High-volume agentic and tool-calling loops where cost per call matters
Gemini 3.1 Flash Lite
Gemini 3.1 Flash Lite lists high-volume agentic and tool-calling loops where cost per call matters among its strengths; Inkling does not.
The first model release from Mira Murati's Thinking Machines Lab (ex-OpenAI CTO) - one of the most closely-watched new labs in AI
Inkling
Open weights make this possible at all — Gemini 3.1 Flash Lite is API-only, so it cannot leave the vendor's servers.
A 975B-parameter MoE (41B active) with native text, image, and audio reasoning in one model
Inkling
Gemini 3.1 Flash Lite is comparatively weak here — lower reasoning and quality ceiling than Gemini 3.1 Pro and the full Gemini 3 Flash tier
A dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request
Inkling
Mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control — and its weights are open while Gemini 3.1 Flash Lite is API-only.
Lowest cost at scale
Inkling
Its weights are open, so at volume you pay for your own hardware instead of Gemini 3.1 Flash Lite's $0.25/$1.5 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Inkling
At Open weight (self-host / free) it undercuts Gemini 3.1 Flash Lite, and on millions of tokens that margin decides the monthly bill.
A team with data-privacy or self-hosting needs
→ Inkling
Open weights let you run it on your own hardware; Gemini 3.1 Flash Lite is API-only.
Anyone whose priority is ultra-low-latency, high-volume production workloads
→ Gemini 3.1 Flash Lite
It is specifically built for that.
Anyone whose priority is the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai
→ Inkling
That is its strongest area.
Gemini 3.1 Flash Lite: where it fits
Google's fastest and most cost-efficient Gemini 3 series model, built for ultra-low-latency, high-volume production workloads at half the price of Gemini 3 Flash. Released March 3, 2026 by Google, it is built for ultra-low-latency, high-volume production workloads, most cost-efficient Gemini 3 model — half the price of Gemini 3 Flash ($0.25/$1.50 vs $0.50/$3.00 per 1M tokens), high-volume agentic and tool-calling loops where cost per call matters, and multimodal input across text, image, video, audio, and PDF.
Its trade-offs are real: lower reasoning and quality ceiling than Gemini 3.1 Pro and the full Gemini 3 Flash tier, sharp long-context degradation — MRCR v2 (8-needle) retrieval falls to ~12% at the full 1M-token window, and closed weights — not downloadable or self-hostable. At $0.25 in / $1.5 out per million tokens, it sits in the budget price band.
Inkling: where it fits
Mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control. Released July 15, 2026 by Thinking Machines Lab, it is built for the first model release from Mira Murati's Thinking Machines Lab (ex-OpenAI CTO) - one of the most closely-watched new labs in AI, a 975B-parameter MoE (41B active) with native text, image, and audio reasoning in one model, a dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request, and fully open weights (Apache 2.0) at frontier scale - unusual for a model this large and this new.
Its trade-offs: a brand-new lab's first release - no multi-generation track record yet, no official hosted API price - available via third-party hosts only at launch, and independent third-party benchmark verification is still limited given how recently it shipped. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
The defining split here is open vs. closed. Inkling gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Gemini 3.1 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 3.1 Flash Lite and Inkling 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 3.1 Flash Lite or Inkling better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Gemini 3.1 Flash Lite leans toward ultra-low-latency, high-volume production workloads while Inkling leans toward the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemini 3.1 Flash Lite or Inkling?
Inkling is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Gemini 3.1 Flash Lite is API-metered at $0.25/$1.5 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?
Both advertise 1M (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Gemini 3.1 Flash Lite and Inkling together?
Yes — a multi-model platform like LumiChats gives you Gemini 3.1 Flash Lite, Inkling 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 Flash Lite or Inkling?
Inkling — released July 15, 2026, about 4 months after Gemini 3.1 Flash Lite.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.