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. Pick Qwen3.6 35B A3B for extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost or runs at roughly 120 tokens per second on a single 24gb consumer gpu.
Jamba2 Mini (AI21 Labs, Israel) and Qwen3.6 35B A3B (Alibaba, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. Qwen3.6 35B A3B is a sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware. The breakdown below works through their capabilities and ideal use cases so you can match one to your task.
Key differences
Context window: both advertise 256K (~393 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
Recency: Qwen3.6 35B A3B is the newer model by about 3 months (released April 16, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a Israel-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
Jamba2 Mini
Qwen3.6 35B A3B
Provider
AI21 Labs (Israel)
Alibaba (China)
Released
January 8, 2026
April 16, 2026
Context window
256K (~393 pages)
256K (~393 pages)
Price (in/out)
Open weight (self-host / free)
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, code
SWE-Bench Verified
Not published
73.4%
MRCR v2 @ 1M
Not published
Not published
Who wins what
A genuinely different architecture - hybrid Mamba (SSM) + Transformer, not another transformer variant: Jamba2 Mini — Jamba2 Mini lists a genuinely different architecture - hybrid Mamba (SSM) + Transformer, not another transformer variant among its strengths; Qwen3.6 35B A3B does not.
Fast, efficient long-context inference tuned for enterprise workloads: Jamba2 Mini — Jamba2 Mini lists fast, efficient long-context inference tuned for enterprise workloads among its strengths; Qwen3.6 35B A3B does not.
Open weights (Apache 2.0) - free to self-host: Jamba2 Mini — Jamba2 Mini lists open weights (Apache 2.0) - free to self-host among its strengths; Qwen3.6 35B A3B does not.
Extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost: Qwen3.6 35B A3B — Jamba2 Mini is comparatively weak here — no official per-token API price published - self-host or use AI21's platform preview
Runs at roughly 120 tokens per second on a single 24GB consumer GPU: Qwen3.6 35B A3B — A sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware — and it is the newer of the two.
Apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN: Qwen3.6 35B A3B — Qwen3.6 35B A3B lists apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN among its strengths; Jamba2 Mini does not.
Which should you pick?
Anyone whose priority is a genuinely different architecture - hybrid mamba (ssm) + transformer, not another transformer variant: Jamba2 Mini — It is specifically built for that.
Anyone whose priority is extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost: Qwen3.6 35B A3B — That is its strongest area.
An enterprise with regional data-residency rules: Qwen3.6 35B A3B or Jamba2 Mini — Origin (Israel vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: 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.
Qwen3.6 35B A3B: where it fits
A sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware. Released April 16, 2026 by Alibaba, it is built for extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost, runs at roughly 120 tokens per second on a single 24GB consumer GPU, apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN, and preserves its reasoning across turns, which cuts the overhead of agentic loops.
Its trade-offs: loses to its smaller dense sibling Qwen3.6 27B on every coding benchmark, despite more total parameters, its SWE-Bench score comes from Alibaba's internal scaffold rather than the standard public harness, and all 35B parameters must stay resident in VRAM even though only 3B compute per token. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." Jamba2 Mini (Israel) and Qwen3.6 35B A3B (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Frequently asked questions
Is Jamba2 Mini or Qwen3.6 35B A3B better for coding?
Public SWE-Bench figures are not available for Jamba2 Mini, so the honest test is your own repository — run an identical real bug through both. By design, Jamba2 Mini leans toward a genuinely different architecture - hybrid mamba (ssm) + transformer, not another transformer variant while Qwen3.6 35B A3B leans toward extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Jamba2 Mini or Qwen3.6 35B A3B?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
Both advertise 256K (~393 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Jamba2 Mini and Qwen3.6 35B A3B together?
Yes — a multi-model platform like LumiChats gives you Jamba2 Mini, Qwen3.6 35B A3B 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, Jamba2 Mini or Qwen3.6 35B A3B?
Qwen3.6 35B A3B — released April 16, 2026, about 3 months after Jamba2 Mini.
Jamba2 Mini vs Qwen3.6 35B A3B
AI21 Labs · Israel | Alibaba · China · Updated June 2026
Quick verdict
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. Pick Qwen3.6 35B A3B for extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost or runs at roughly 120 tokens per second on a single 24gb consumer gpu.
Jamba2 Mini (AI21 Labs, Israel) and Qwen3.6 35B A3B (Alibaba, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. Qwen3.6 35B A3B is a sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware. The breakdown below works through their capabilities and ideal use cases so you can match one to your task.
Key differences at a glance
▸Context window: both advertise 256K (~393 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
▸Recency: Qwen3.6 35B A3B is the newer model by about 3 months (released April 16, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a Israel-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
Jamba2 Mini
Qwen3.6 35B A3B
Provider
AI21 Labs (Israel)
Alibaba (China)
Released
January 8, 2026
April 16, 2026
Context window
256K (~393 pages)
256K (~393 pages)
Price (in/out)
Open weight (self-host / free)
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, code
SWE-Bench Verified
Not published
73.4%
MRCR v2 @ 1M
Not published
Not published
Who wins what
A genuinely different architecture - hybrid Mamba (SSM) + Transformer, not another transformer variant
Jamba2 Mini
Jamba2 Mini lists a genuinely different architecture - hybrid Mamba (SSM) + Transformer, not another transformer variant among its strengths; Qwen3.6 35B A3B does not.
Fast, efficient long-context inference tuned for enterprise workloads
Jamba2 Mini
Jamba2 Mini lists fast, efficient long-context inference tuned for enterprise workloads among its strengths; Qwen3.6 35B A3B does not.
Open weights (Apache 2.0) - free to self-host
Jamba2 Mini
Jamba2 Mini lists open weights (Apache 2.0) - free to self-host among its strengths; Qwen3.6 35B A3B does not.
Extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost
Qwen3.6 35B A3B
Jamba2 Mini is comparatively weak here — no official per-token API price published - self-host or use AI21's platform preview
Runs at roughly 120 tokens per second on a single 24GB consumer GPU
Qwen3.6 35B A3B
A sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware — and it is the newer of the two.
Apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN
Qwen3.6 35B A3B
Qwen3.6 35B A3B lists apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN among its strengths; Jamba2 Mini does not.
Which should you pick?
Anyone whose priority is a genuinely different architecture - hybrid mamba (ssm) + transformer, not another transformer variant
→ Jamba2 Mini
It is specifically built for that.
Anyone whose priority is extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost
→ Qwen3.6 35B A3B
That is its strongest area.
An enterprise with regional data-residency rules
→ Qwen3.6 35B A3B or Jamba2 Mini
Origin (Israel vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: 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.
Qwen3.6 35B A3B: where it fits
A sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware. Released April 16, 2026 by Alibaba, it is built for extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost, runs at roughly 120 tokens per second on a single 24GB consumer GPU, apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN, and preserves its reasoning across turns, which cuts the overhead of agentic loops.
Its trade-offs: loses to its smaller dense sibling Qwen3.6 27B on every coding benchmark, despite more total parameters, its SWE-Bench score comes from Alibaba's internal scaffold rather than the standard public harness, and all 35B parameters must stay resident in VRAM even though only 3B compute per token. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." Jamba2 Mini (Israel) and Qwen3.6 35B A3B (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Want both Jamba2 Mini and Qwen3.6 35B A3B 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 Jamba2 Mini or Qwen3.6 35B A3B better for coding?
Public SWE-Bench figures are not available for Jamba2 Mini, so the honest test is your own repository — run an identical real bug through both. By design, Jamba2 Mini leans toward a genuinely different architecture - hybrid mamba (ssm) + transformer, not another transformer variant while Qwen3.6 35B A3B leans toward extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Jamba2 Mini or Qwen3.6 35B A3B?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
Both advertise 256K (~393 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Jamba2 Mini and Qwen3.6 35B A3B together?
Yes — a multi-model platform like LumiChats gives you Jamba2 Mini, Qwen3.6 35B A3B 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, Jamba2 Mini or Qwen3.6 35B A3B?
Qwen3.6 35B A3B — released April 16, 2026, about 3 months 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.