Pick Gemini 2.5 Flash for cheapest 1m-context option or very fast. Pick Llama 4 Maverick for open weights, 1m context or strong image + text understanding. Choose Llama 4 Maverick if you need self-hosting or data privacy; Gemini 2.5 Flash if you want a managed API.
Gemini 2.5 Flash (Google) and Llama 4 Maverick (Meta) are two of the models people most often weigh against each other in 2026. Gemini 2.5 Flash is google's ultra-cheap, fast 1M-context model for high-volume multimodal work. Llama 4 Maverick is meta's open-weight 1M-context multimodal model for self-hosted deployments. They diverge most on price and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Cost model: Llama 4 Maverick ships open weights you can self-host (hardware cost only, no per-token fee), while Gemini 2.5 Flash is API-metered at $0.3/$2.5 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: Gemini 2.5 Flash is the newer model by about 57 days (released June 2025), usually meaning fresher training data and capabilities.
Specifications
Spec
Gemini 2.5 Flash
Llama 4 Maverick
Provider
Google (US)
Meta (US)
Released
June 2025
April 2025
Context window
1M (~1,500 pages)
1M (~1,500 pages)
Price (in/out)
$0.3/$2.5 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, audio, video, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Cheapest 1M-context option: Gemini 2.5 Flash — Google's ultra-cheap, fast 1M-context model for high-volume multimodal work — and it is the newer of the two.
Very fast: Gemini 2.5 Flash — Gemini 2.5 Flash lists very fast among its strengths; Llama 4 Maverick does not.
High-volume multimodal: Gemini 2.5 Flash — Gemini 2.5 Flash lists high-volume multimodal among its strengths; Llama 4 Maverick does not.
Open weights, 1M context: Llama 4 Maverick — Open weights make this possible at all — Gemini 2.5 Flash is API-only, so it cannot leave the vendor's servers.
Strong image + text understanding: Llama 4 Maverick — Meta's open-weight 1M-context multimodal model for self-hosted deployments — and its weights are open while Gemini 2.5 Flash is API-only.
Self-hostable: Llama 4 Maverick — Llama 4 Maverick lists self-hostable among its strengths; Gemini 2.5 Flash does not.
Lowest cost at scale: Llama 4 Maverick — Its weights are open, so at volume you pay for your own hardware instead of Gemini 2.5 Flash's $0.3/$2.5 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume: Llama 4 Maverick — At Open weight (self-host / free) it undercuts Gemini 2.5 Flash, and on millions of tokens that margin decides the monthly bill.
A team with data-privacy or self-hosting needs: Llama 4 Maverick — Open weights let you run it on your own hardware; Gemini 2.5 Flash is API-only.
Anyone whose priority is cheapest 1m-context option: Gemini 2.5 Flash — It is specifically built for that.
Anyone whose priority is open weights, 1m context: Llama 4 Maverick — That is its strongest area.
Gemini 2.5 Flash: where it fits
Google's ultra-cheap, fast 1M-context model for high-volume multimodal work. Released June 2025 by Google, it is built for cheapest 1M-context option, very fast, high-volume multimodal, and workspace integration.
Its trade-offs are real: lighter reasoning than Pro tiers, and superseded by 3.5 Flash. At $0.3 in / $2.5 out per million tokens, it sits in the budget price band.
Llama 4 Maverick: where it fits
Meta's open-weight 1M-context multimodal model for self-hosted deployments. Released April 2025 by Meta, it is built for open weights, 1M context, strong image + text understanding, self-hostable, and 400B MoE, 17B active.
Its trade-offs: needs serious hardware to self-host, and trails closed frontier on 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 Maverick gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Gemini 2.5 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 2.5 Flash or Llama 4 Maverick 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 2.5 Flash leans toward cheapest 1m-context option while Llama 4 Maverick leans toward open weights, 1m context, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemini 2.5 Flash or Llama 4 Maverick?
Llama 4 Maverick is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Gemini 2.5 Flash 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?
Both advertise 1M (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Gemini 2.5 Flash and Llama 4 Maverick together?
Yes — a multi-model platform like LumiChats gives you Gemini 2.5 Flash, Llama 4 Maverick 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 2.5 Flash or Llama 4 Maverick?
Gemini 2.5 Flash — released June 2025, about 57 days after Llama 4 Maverick.
Gemini 2.5 Flash vs Llama 4 Maverick
Google · US | Meta · US · Updated June 2026
Quick verdict
Pick Gemini 2.5 Flash for cheapest 1m-context option or very fast. Pick Llama 4 Maverick for open weights, 1m context or strong image + text understanding. Choose Llama 4 Maverick if you need self-hosting or data privacy; Gemini 2.5 Flash if you want a managed API.
Gemini 2.5 Flash (Google) and Llama 4 Maverick (Meta) are two of the models people most often weigh against each other in 2026. Gemini 2.5 Flash is google's ultra-cheap, fast 1M-context model for high-volume multimodal work. Llama 4 Maverick is meta's open-weight 1M-context multimodal model for self-hosted deployments. 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: Llama 4 Maverick ships open weights you can self-host (hardware cost only, no per-token fee), while Gemini 2.5 Flash is API-metered at $0.3/$2.5 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: Gemini 2.5 Flash is the newer model by about 57 days (released June 2025), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Gemini 2.5 Flash
Llama 4 Maverick
Provider
Google (US)
Meta (US)
Released
June 2025
April 2025
Context window
1M (~1,500 pages)
1M (~1,500 pages)
Price (in/out)
$0.3/$2.5 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, audio, video, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Cheapest 1M-context option
Gemini 2.5 Flash
Google's ultra-cheap, fast 1M-context model for high-volume multimodal work — and it is the newer of the two.
Very fast
Gemini 2.5 Flash
Gemini 2.5 Flash lists very fast among its strengths; Llama 4 Maverick does not.
High-volume multimodal
Gemini 2.5 Flash
Gemini 2.5 Flash lists high-volume multimodal among its strengths; Llama 4 Maverick does not.
Open weights, 1M context
Llama 4 Maverick
Open weights make this possible at all — Gemini 2.5 Flash is API-only, so it cannot leave the vendor's servers.
Strong image + text understanding
Llama 4 Maverick
Meta's open-weight 1M-context multimodal model for self-hosted deployments — and its weights are open while Gemini 2.5 Flash is API-only.
Self-hostable
Llama 4 Maverick
Llama 4 Maverick lists self-hostable among its strengths; Gemini 2.5 Flash does not.
Lowest cost at scale
Llama 4 Maverick
Its weights are open, so at volume you pay for your own hardware instead of Gemini 2.5 Flash's $0.3/$2.5 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Llama 4 Maverick
At Open weight (self-host / free) it undercuts Gemini 2.5 Flash, and on millions of tokens that margin decides the monthly bill.
A team with data-privacy or self-hosting needs
→ Llama 4 Maverick
Open weights let you run it on your own hardware; Gemini 2.5 Flash is API-only.
Anyone whose priority is cheapest 1m-context option
→ Gemini 2.5 Flash
It is specifically built for that.
Anyone whose priority is open weights, 1m context
→ Llama 4 Maverick
That is its strongest area.
Gemini 2.5 Flash: where it fits
Google's ultra-cheap, fast 1M-context model for high-volume multimodal work. Released June 2025 by Google, it is built for cheapest 1M-context option, very fast, high-volume multimodal, and workspace integration.
Its trade-offs are real: lighter reasoning than Pro tiers, and superseded by 3.5 Flash. At $0.3 in / $2.5 out per million tokens, it sits in the budget price band.
Llama 4 Maverick: where it fits
Meta's open-weight 1M-context multimodal model for self-hosted deployments. Released April 2025 by Meta, it is built for open weights, 1M context, strong image + text understanding, self-hostable, and 400B MoE, 17B active.
Its trade-offs: needs serious hardware to self-host, and trails closed frontier on 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 Maverick gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Gemini 2.5 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 2.5 Flash and Llama 4 Maverick 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 2.5 Flash or Llama 4 Maverick 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 2.5 Flash leans toward cheapest 1m-context option while Llama 4 Maverick leans toward open weights, 1m context, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemini 2.5 Flash or Llama 4 Maverick?
Llama 4 Maverick is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Gemini 2.5 Flash 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?
Both advertise 1M (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Gemini 2.5 Flash and Llama 4 Maverick together?
Yes — a multi-model platform like LumiChats gives you Gemini 2.5 Flash, Llama 4 Maverick 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 2.5 Flash or Llama 4 Maverick?
Gemini 2.5 Flash — released June 2025, about 57 days after Llama 4 Maverick.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.