Pick Gemini 3.1 Pro for full multimodal input — text, image, audio and video in one 1m-token window or long video and document analysis. Pick Jamba2 Mini for a genuinely different architecture - hybrid mamba (ssm) + transformer, not another transformer variant or fast, efficient long-context inference tuned for enterprise workloads. Choose Jamba2 Mini 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 Jamba2 Mini (AI21 Labs, Israel) 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 1M-token multimodal workhorse — huge breadth across text, image, audio and video, but recall fades deep in the window. Jamba2 Mini is aI21's hybrid Mamba-Transformer model - a genuinely different architecture built for fast, efficient long-context enterprise work, free to self-host under Apache 2.0. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Cost model: Jamba2 Mini ships open weights you can self-host (hardware cost only, no per-token fee), while Gemini 3.1 Pro is API-metered at $2/$12 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
Context window: Gemini 3.1 Pro holds 4× more — 1M (~1,573 pages) vs 256K (~393 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 42 days (released February 19, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a US-vs-Israel matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
Gemini 3.1 Pro
Jamba2 Mini
Provider
Google (US)
AI21 Labs (Israel)
Released
February 19, 2026
January 8, 2026
Context window
1M (~1,573 pages)
256K (~393 pages)
Price (in/out)
$2/$12 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, audio, video, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
26.3%
Not published
Who wins what
Full multimodal input — text, image, audio and video in one 1M-token window: Gemini 3.1 Pro — Its 1M window holds about 4× more than Jamba2 Mini's 256K in a single prompt.
Long video and document analysis: Gemini 3.1 Pro — A 1M-token multimodal workhorse — huge breadth across text, image, audio and video, but recall fades deep in the window — and it carries the larger 1M context.
Agentic reasoning (high ARC-AGI-2): Gemini 3.1 Pro — Jamba2 Mini is comparatively weak here — tuned for grounded, steerable enterprise tasks rather than topping general reasoning leaderboards
A genuinely different architecture - hybrid Mamba (SSM) + Transformer, not another transformer variant: Jamba2 Mini — AI21's hybrid Mamba-Transformer model - a genuinely different architecture built for fast, efficient long-context enterprise work, free to self-host under Apache 2.0 — and its weights are open while Gemini 3.1 Pro is API-only.
Fast, efficient long-context inference tuned for enterprise workloads: Jamba2 Mini — Gemini 3.1 Pro is comparatively weak here — long-context recall drops sharply past 256K (26.3% on MRCR v2 at 1M)
Open weights (Apache 2.0) - free to self-host: Jamba2 Mini — Open weights make this possible at all — Gemini 3.1 Pro is API-only, so it cannot leave the vendor's servers.
Lowest cost at scale: Jamba2 Mini — Its weights are open, so at volume you pay for your own hardware instead of Gemini 3.1 Pro's $2/$12 per 1M tokens.
Largest single-prompt input: Gemini 3.1 Pro — Its 1M window is about 4× larger than Jamba2 Mini's 256K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Jamba2 Mini — At Open weight (self-host / free) 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 1M window fits more in one prompt.
A team with data-privacy or self-hosting needs: Jamba2 Mini — Open weights let you run it on your own hardware; Gemini 3.1 Pro is API-only.
Anyone whose priority is full multimodal input — text, image, audio and video in one 1m-token window: Gemini 3.1 Pro — It is specifically built for that.
Anyone whose priority is a genuinely different architecture - hybrid mamba (ssm) + transformer, not another transformer variant: Jamba2 Mini — That is its strongest area.
An enterprise with regional data-residency rules: Gemini 3.1 Pro or Jamba2 Mini — Origin (US vs Israel) 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 1M-token multimodal workhorse — huge breadth across text, image, audio and video, but recall fades deep in the window. Released February 19, 2026 by Google, it is built for full multimodal input — text, image, audio and video in one 1M-token window, long video and document analysis, agentic reasoning (high ARC-AGI-2), and broad multimodal understanding across formats.
Its trade-offs are real: long-context recall drops sharply past 256K (26.3% on MRCR v2 at 1M), premium price per token at $2/$12, and superseded within Google's own line by the newer 3.5/3.6 Flash releases for cost-sensitive work. At $2 in / $12 out per million tokens, it sits in the mid price band.
Jamba2 Mini: where it fits
AI21's hybrid Mamba-Transformer model - a genuinely different architecture built for fast, efficient long-context enterprise work, free to self-host under Apache 2.0. Released January 8, 2026 by AI21 Labs, it is built for a genuinely different architecture - hybrid Mamba (SSM) + Transformer, not another transformer variant, fast, efficient long-context inference tuned for enterprise workloads, open weights (Apache 2.0) - free to self-host, and a 52B-total/12B-active MoE design that's cheap to run relative to its capability.
Its trade-offs: tuned for grounded, steerable enterprise tasks rather than topping general reasoning leaderboards, no official per-token API price published - self-host or use AI21's platform preview, and newer architecture family means less third-party tooling support than mainstream transformers. 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. Jamba2 Mini 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 Jamba2 Mini 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 Pro leans toward full multimodal input — text, image, audio and video in one 1m-token window while Jamba2 Mini leans toward a genuinely different architecture - hybrid mamba (ssm) + transformer, not another transformer variant, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemini 3.1 Pro or Jamba2 Mini?
Jamba2 Mini 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 — 1M vs 256K, about 4× 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 Jamba2 Mini together?
Yes — a multi-model platform like LumiChats gives you Gemini 3.1 Pro, Jamba2 Mini 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 Jamba2 Mini?
Gemini 3.1 Pro — released February 19, 2026, about 42 days after Jamba2 Mini.
Gemini 3.1 Pro vs Jamba2 Mini
Google · US | AI21 Labs · Israel · Updated June 2026
Quick verdict
Pick Gemini 3.1 Pro for full multimodal input — text, image, audio and video in one 1m-token window or long video and document analysis. Pick Jamba2 Mini for a genuinely different architecture - hybrid mamba (ssm) + transformer, not another transformer variant or fast, efficient long-context inference tuned for enterprise workloads. Choose Jamba2 Mini 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 Jamba2 Mini (AI21 Labs, Israel) 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 1M-token multimodal workhorse — huge breadth across text, image, audio and video, but recall fades deep in the window. Jamba2 Mini is aI21's hybrid Mamba-Transformer model - a genuinely different architecture built for fast, efficient long-context enterprise work, free to self-host under Apache 2.0. 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
▸Cost model: Jamba2 Mini ships open weights you can self-host (hardware cost only, no per-token fee), while Gemini 3.1 Pro is API-metered at $2/$12 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
▸Context window: Gemini 3.1 Pro holds 4× more — 1M (~1,573 pages) vs 256K (~393 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 42 days (released February 19, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a US-vs-Israel matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
Gemini 3.1 Pro
Jamba2 Mini
Provider
Google (US)
AI21 Labs (Israel)
Released
February 19, 2026
January 8, 2026
Context window
1M (~1,573 pages)
256K (~393 pages)
Price (in/out)
$2/$12 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, audio, video, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
26.3%
Not published
Who wins what
Full multimodal input — text, image, audio and video in one 1M-token window
Gemini 3.1 Pro
Its 1M window holds about 4× more than Jamba2 Mini's 256K in a single prompt.
Long video and document analysis
Gemini 3.1 Pro
A 1M-token multimodal workhorse — huge breadth across text, image, audio and video, but recall fades deep in the window — and it carries the larger 1M context.
Agentic reasoning (high ARC-AGI-2)
Gemini 3.1 Pro
Jamba2 Mini is comparatively weak here — tuned for grounded, steerable enterprise tasks rather than topping general reasoning leaderboards
A genuinely different architecture - hybrid Mamba (SSM) + Transformer, not another transformer variant
Jamba2 Mini
AI21's hybrid Mamba-Transformer model - a genuinely different architecture built for fast, efficient long-context enterprise work, free to self-host under Apache 2.0 — and its weights are open while Gemini 3.1 Pro is API-only.
Fast, efficient long-context inference tuned for enterprise workloads
Jamba2 Mini
Gemini 3.1 Pro is comparatively weak here — long-context recall drops sharply past 256K (26.3% on MRCR v2 at 1M)
Open weights (Apache 2.0) - free to self-host
Jamba2 Mini
Open weights make this possible at all — Gemini 3.1 Pro is API-only, so it cannot leave the vendor's servers.
Lowest cost at scale
Jamba2 Mini
Its weights are open, so at volume you pay for your own hardware instead of Gemini 3.1 Pro's $2/$12 per 1M tokens.
Largest single-prompt input
Gemini 3.1 Pro
Its 1M window is about 4× larger than Jamba2 Mini's 256K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Jamba2 Mini
At Open weight (self-host / free) 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 1M window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ Jamba2 Mini
Open weights let you run it on your own hardware; Gemini 3.1 Pro is API-only.
Anyone whose priority is full multimodal input — text, image, audio and video in one 1m-token window
→ Gemini 3.1 Pro
It is specifically built for that.
Anyone whose priority is a genuinely different architecture - hybrid mamba (ssm) + transformer, not another transformer variant
→ Jamba2 Mini
That is its strongest area.
An enterprise with regional data-residency rules
→ Gemini 3.1 Pro or Jamba2 Mini
Origin (US vs Israel) 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 1M-token multimodal workhorse — huge breadth across text, image, audio and video, but recall fades deep in the window. Released February 19, 2026 by Google, it is built for full multimodal input — text, image, audio and video in one 1M-token window, long video and document analysis, agentic reasoning (high ARC-AGI-2), and broad multimodal understanding across formats.
Its trade-offs are real: long-context recall drops sharply past 256K (26.3% on MRCR v2 at 1M), premium price per token at $2/$12, and superseded within Google's own line by the newer 3.5/3.6 Flash releases for cost-sensitive work. At $2 in / $12 out per million tokens, it sits in the mid price band.
Jamba2 Mini: where it fits
AI21's hybrid Mamba-Transformer model - a genuinely different architecture built for fast, efficient long-context enterprise work, free to self-host under Apache 2.0. Released January 8, 2026 by AI21 Labs, it is built for a genuinely different architecture - hybrid Mamba (SSM) + Transformer, not another transformer variant, fast, efficient long-context inference tuned for enterprise workloads, open weights (Apache 2.0) - free to self-host, and a 52B-total/12B-active MoE design that's cheap to run relative to its capability.
Its trade-offs: tuned for grounded, steerable enterprise tasks rather than topping general reasoning leaderboards, no official per-token API price published - self-host or use AI21's platform preview, and newer architecture family means less third-party tooling support than mainstream transformers. 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. Jamba2 Mini 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 Jamba2 Mini 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 Pro or Jamba2 Mini 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 Pro leans toward full multimodal input — text, image, audio and video in one 1m-token window while Jamba2 Mini leans toward a genuinely different architecture - hybrid mamba (ssm) + transformer, not another transformer variant, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemini 3.1 Pro or Jamba2 Mini?
Jamba2 Mini 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 — 1M vs 256K, about 4× 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 Jamba2 Mini together?
Yes — a multi-model platform like LumiChats gives you Gemini 3.1 Pro, Jamba2 Mini 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 Jamba2 Mini?
Gemini 3.1 Pro — released February 19, 2026, about 42 days after Jamba2 Mini.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.