Pick Gemini 3.8 Flash for long-horizon agentic coding (deepswe v1.1) or cost-efficient workhorse performance beating larger models at same price as 3.7 flash. Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly. Choose Llama 4 Scout if you need self-hosting or data privacy; Gemini 3.8 Flash if you want a managed API.
Gemini 3.8 Flash (Google DeepMind) and Llama 4 Scout (Meta) are two of the models people most often weigh against each other in 2026. Gemini 3.8 Flash is google's cost-efficient workhorse model released September 2, 2026, tuned for long-horizon coding and autonomous agents, launched alongside a restricted 'Cyber' variant for vulnerability research. Llama 4 Scout is the 10M-token open-weight giant — enormous on paper, but usable recall is far smaller. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Cost model: Llama 4 Scout ships open weights you can self-host (hardware cost only, no per-token fee), while Gemini 3.8 Flash is API-metered at $0.75/$3.75 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
Context window: Llama 4 Scout holds 10× more — 10M (~15,000 pages) vs 1M tokens (~1,500 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 3.8 Flash is the newer model by about 17 months (released September 2, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Gemini 3.8 Flash
Llama 4 Scout
Provider
Google DeepMind (US)
Meta (US)
Released
September 2, 2026
April 2025
Context window
1M tokens (~1,500 pages)
10M (~15,000 pages)
Price (in/out)
$0.75/$3.75 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, audio, video
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
15%
Who wins what
Long-horizon agentic coding (DeepSWE v1.1): Gemini 3.8 Flash — Google's cost-efficient workhorse model released September 2, 2026, tuned for long-horizon coding and autonomous agents, launched alongside a restricted 'Cyber' variant for vulnerability research — and it is the newer of the two.
Cost-efficient workhorse performance beating larger models at same price as 3.7 Flash: Gemini 3.8 Flash — Gemini 3.8 Flash lists cost-efficient workhorse performance beating larger models at same price as 3.7 Flash among its strengths; Llama 4 Scout does not.
Vals Finance Agent V2 benchmark (61.4%, ahead of Claude Opus 5's 58.6%); also leads a weak field on the Harvey legal benchmark (10.0%): Gemini 3.8 Flash — Gemini 3.8 Flash lists vals Finance Agent V2 benchmark (61.4%, ahead of Claude Opus 5's 58.6%); also leads a weak field on the Harvey legal benchmark (10.0%) among its strengths; Llama 4 Scout does not.
Largest advertised context (10M): Llama 4 Scout — Its 10M window holds about 10× more than Gemini 3.8 Flash's 1M tokens in a single prompt.
Open weights, single-GPU friendly: Llama 4 Scout — Open weights make this possible at all — Gemini 3.8 Flash is API-only, so it cannot leave the vendor's servers.
Self-hosted, data-private deployment: Llama 4 Scout — The 10M-token open-weight giant — enormous on paper, but usable recall is far smaller — and it carries the larger 10M context.
Lowest cost at scale: Llama 4 Scout — Its weights are open, so at volume you pay for your own hardware instead of Gemini 3.8 Flash's $0.75/$3.75 per 1M tokens.
Largest single-prompt input: Llama 4 Scout — Its 10M window is about 10× larger than Gemini 3.8 Flash's 1M tokens, fitting roughly 15,000 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Llama 4 Scout — At Open weight (self-host / free) it undercuts Gemini 3.8 Flash, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Llama 4 Scout — Larger 10M window fits more in one prompt.
A team with data-privacy or self-hosting needs: Llama 4 Scout — Open weights let you run it on your own hardware; Gemini 3.8 Flash is API-only.
Anyone whose priority is long-horizon agentic coding (deepswe v1.1): Gemini 3.8 Flash — It is specifically built for that.
Anyone whose priority is largest advertised context (10m): Llama 4 Scout — That is its strongest area.
Gemini 3.8 Flash: where it fits
Google's cost-efficient workhorse model released September 2, 2026, tuned for long-horizon coding and autonomous agents, launched alongside a restricted 'Cyber' variant for vulnerability research. Released September 2, 2026 by Google DeepMind, it is built for long-horizon agentic coding (DeepSWE v1.1), cost-efficient workhorse performance beating larger models at same price as 3.7 Flash, vals Finance Agent V2 benchmark (61.4%, ahead of Claude Opus 5's 58.6%); also leads a weak field on the Harvey legal benchmark (10.0%), and prompt-injection robustness (Gray Swan benchmark).
Its trade-offs are real: still a mid-tier 'Flash' model, not Google's frontier flagship (which remains unreleased), introductory price doubles on January 1, 2027, hLE-Verified score (54.9%) trails top frontier reasoning models, and built on the same base model as Gemini 3.7 Flash (a post-training update, not a freshly pretrained model). At $0.75 in / $3.75 out per million tokens, it sits in the budget price band.
Llama 4 Scout: where it fits
The 10M-token open-weight giant — enormous on paper, but usable recall is far smaller. Released April 2025 by Meta, it is built for largest advertised context (10M), open weights, single-GPU friendly, self-hosted, data-private deployment, and retrieval over very long inputs.
Its trade-offs: effective recall degrades far below 10M, and ~15% on long-context multi-needle reasoning. 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. Llama 4 Scout gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Gemini 3.8 Flash 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 3.8 Flash or Llama 4 Scout 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 3.8 Flash leans toward long-horizon agentic coding (deepswe v1.1) while Llama 4 Scout leans toward largest advertised context (10m), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemini 3.8 Flash or Llama 4 Scout?
Llama 4 Scout is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Gemini 3.8 Flash is API-metered at $0.75/$3.75 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?
Llama 4 Scout — 10M vs 1M tokens, about 10× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Gemini 3.8 Flash and Llama 4 Scout together?
Yes — a multi-model platform like LumiChats gives you Gemini 3.8 Flash, Llama 4 Scout 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 3.8 Flash or Llama 4 Scout?
Gemini 3.8 Flash — released September 2, 2026, about 17 months after Llama 4 Scout.
Gemini 3.8 Flash vs Llama 4 Scout
Google DeepMind · US | Meta · US · Updated June 2026
Quick verdict
Pick Gemini 3.8 Flash for long-horizon agentic coding (deepswe v1.1) or cost-efficient workhorse performance beating larger models at same price as 3.7 flash. Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly. Choose Llama 4 Scout if you need self-hosting or data privacy; Gemini 3.8 Flash if you want a managed API.
Gemini 3.8 Flash (Google DeepMind) and Llama 4 Scout (Meta) are two of the models people most often weigh against each other in 2026. Gemini 3.8 Flash is google's cost-efficient workhorse model released September 2, 2026, tuned for long-horizon coding and autonomous agents, launched alongside a restricted 'Cyber' variant for vulnerability research. Llama 4 Scout is the 10M-token open-weight giant — enormous on paper, but usable recall is far smaller. 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: Llama 4 Scout ships open weights you can self-host (hardware cost only, no per-token fee), while Gemini 3.8 Flash is API-metered at $0.75/$3.75 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
▸Context window: Llama 4 Scout holds 10× more — 10M (~15,000 pages) vs 1M tokens (~1,500 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 3.8 Flash is the newer model by about 17 months (released September 2, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Gemini 3.8 Flash
Llama 4 Scout
Provider
Google DeepMind (US)
Meta (US)
Released
September 2, 2026
April 2025
Context window
1M tokens (~1,500 pages)
10M (~15,000 pages)
Price (in/out)
$0.75/$3.75 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, audio, video
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
15%
Who wins what
Long-horizon agentic coding (DeepSWE v1.1)
Gemini 3.8 Flash
Google's cost-efficient workhorse model released September 2, 2026, tuned for long-horizon coding and autonomous agents, launched alongside a restricted 'Cyber' variant for vulnerability research — and it is the newer of the two.
Cost-efficient workhorse performance beating larger models at same price as 3.7 Flash
Gemini 3.8 Flash
Gemini 3.8 Flash lists cost-efficient workhorse performance beating larger models at same price as 3.7 Flash among its strengths; Llama 4 Scout does not.
Vals Finance Agent V2 benchmark (61.4%, ahead of Claude Opus 5's 58.6%); also leads a weak field on the Harvey legal benchmark (10.0%)
Gemini 3.8 Flash
Gemini 3.8 Flash lists vals Finance Agent V2 benchmark (61.4%, ahead of Claude Opus 5's 58.6%); also leads a weak field on the Harvey legal benchmark (10.0%) among its strengths; Llama 4 Scout does not.
Largest advertised context (10M)
Llama 4 Scout
Its 10M window holds about 10× more than Gemini 3.8 Flash's 1M tokens in a single prompt.
Open weights, single-GPU friendly
Llama 4 Scout
Open weights make this possible at all — Gemini 3.8 Flash is API-only, so it cannot leave the vendor's servers.
Self-hosted, data-private deployment
Llama 4 Scout
The 10M-token open-weight giant — enormous on paper, but usable recall is far smaller — and it carries the larger 10M context.
Lowest cost at scale
Llama 4 Scout
Its weights are open, so at volume you pay for your own hardware instead of Gemini 3.8 Flash's $0.75/$3.75 per 1M tokens.
Largest single-prompt input
Llama 4 Scout
Its 10M window is about 10× larger than Gemini 3.8 Flash's 1M tokens, fitting roughly 15,000 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Llama 4 Scout
At Open weight (self-host / free) it undercuts Gemini 3.8 Flash, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Llama 4 Scout
Larger 10M window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ Llama 4 Scout
Open weights let you run it on your own hardware; Gemini 3.8 Flash is API-only.
Anyone whose priority is long-horizon agentic coding (deepswe v1.1)
→ Gemini 3.8 Flash
It is specifically built for that.
Anyone whose priority is largest advertised context (10m)
→ Llama 4 Scout
That is its strongest area.
Gemini 3.8 Flash: where it fits
Google's cost-efficient workhorse model released September 2, 2026, tuned for long-horizon coding and autonomous agents, launched alongside a restricted 'Cyber' variant for vulnerability research. Released September 2, 2026 by Google DeepMind, it is built for long-horizon agentic coding (DeepSWE v1.1), cost-efficient workhorse performance beating larger models at same price as 3.7 Flash, vals Finance Agent V2 benchmark (61.4%, ahead of Claude Opus 5's 58.6%); also leads a weak field on the Harvey legal benchmark (10.0%), and prompt-injection robustness (Gray Swan benchmark).
Its trade-offs are real: still a mid-tier 'Flash' model, not Google's frontier flagship (which remains unreleased), introductory price doubles on January 1, 2027, hLE-Verified score (54.9%) trails top frontier reasoning models, and built on the same base model as Gemini 3.7 Flash (a post-training update, not a freshly pretrained model). At $0.75 in / $3.75 out per million tokens, it sits in the budget price band.
Llama 4 Scout: where it fits
The 10M-token open-weight giant — enormous on paper, but usable recall is far smaller. Released April 2025 by Meta, it is built for largest advertised context (10M), open weights, single-GPU friendly, self-hosted, data-private deployment, and retrieval over very long inputs.
Its trade-offs: effective recall degrades far below 10M, and ~15% on long-context multi-needle reasoning. 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. Llama 4 Scout gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Gemini 3.8 Flash 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 3.8 Flash and Llama 4 Scout 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 3.8 Flash or Llama 4 Scout 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 3.8 Flash leans toward long-horizon agentic coding (deepswe v1.1) while Llama 4 Scout leans toward largest advertised context (10m), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemini 3.8 Flash or Llama 4 Scout?
Llama 4 Scout is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Gemini 3.8 Flash is API-metered at $0.75/$3.75 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?
Llama 4 Scout — 10M vs 1M tokens, about 10× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Gemini 3.8 Flash and Llama 4 Scout together?
Yes — a multi-model platform like LumiChats gives you Gemini 3.8 Flash, Llama 4 Scout 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 3.8 Flash or Llama 4 Scout?
Gemini 3.8 Flash — released September 2, 2026, about 17 months after Llama 4 Scout.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.