Gemini 4 Argon vs Llama 4 Maverick
Google · US | Meta · US · Updated June 2026
Quick verdict
Pick Gemini 4 Argon for 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses or google-reported deepswe v1.1 of 77.9% and lvbench long-video understanding of 91.7%. 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 4 Argon if you want a managed API.
Gemini 4 Argon (Google) and Llama 4 Maverick (Meta) are two of the models people most often weigh against each other in 2026. Gemini 4 Argon is google's Gemini 4 Argon, broadly available October 13, 2026 - a 1M-context model at $2/$10 introductory pricing (doubling December 1). Llama 4 Maverick is meta's open-weight 1M-context multimodal model for self-hosted deployments. 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 Maverick ships open weights you can self-host (hardware cost only, no per-token fee), while Gemini 4 Argon is API-metered at $2/$10 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
- ▸Context window: 1M tokens vs 1M — within a few percent of each other, so treat this as a tie and test on your own long inputs, since usable recall varies by model.
- ▸Recency: Gemini 4 Argon is the newer model by about 19 months (released October 13, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | Gemini 4 Argon | Llama 4 Maverick |
|---|---|---|
| Provider | Google (US) | Meta (US) |
| Released | October 13, 2026 | April 2025 |
| Context window | 1M tokens (~1,573 pages) | 1M (~1,500 pages) |
| Price (in/out) | $2/$10 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
1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses
Gemini 4 Argon
Google's Gemini 4 Argon, broadly available October 13, 2026 - a 1M-context model at $2/$10 introductory pricing (doubling December 1) — and it is the newer of the two.
Google-reported DeepSWE v1.1 of 77.9% and LVBench long-video understanding of 91.7%
Gemini 4 Argon
Gemini 4 Argon lists google-reported DeepSWE v1.1 of 77.9% and LVBench long-video understanding of 91.7% among its strengths; Llama 4 Maverick does not.
Artificial Analysis Intelligence Index of 53 at high effort (AI Weekly)
Gemini 4 Argon
Gemini 4 Argon lists artificial Analysis Intelligence Index of 53 at high effort (AI Weekly) among its strengths; Llama 4 Maverick does not.
Open weights, 1M context
Llama 4 Maverick
Open weights make this possible at all — Gemini 4 Argon 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 4 Argon is API-only.
Self-hostable
Llama 4 Maverick
Llama 4 Maverick lists self-hostable among its strengths; Gemini 4 Argon 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 4 Argon's $2/$10 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 4 Argon, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Gemini 4 Argon
Larger 1M tokens window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ Llama 4 Maverick
Open weights let you run it on your own hardware; Gemini 4 Argon is API-only.
Anyone whose priority is 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses
→ Gemini 4 Argon
It is specifically built for that.
Anyone whose priority is open weights, 1m context
→ Llama 4 Maverick
That is its strongest area.
Gemini 4 Argon: where it fits
Google's Gemini 4 Argon, broadly available October 13, 2026 - a 1M-context model at $2/$10 introductory pricing (doubling December 1). Released October 13, 2026 by Google, it is built for 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses, google-reported DeepSWE v1.1 of 77.9% and LVBench long-video understanding of 91.7%, and artificial Analysis Intelligence Index of 53 at high effort (AI Weekly).
Its trade-offs are real: introductory $2/$10 pricing ends November 30; from December 1 it doubles to $4/$20, verbose: about 62K output tokens per Artificial Analysis task, so cost per task is high, and announced September 30 for trusted cyber defenders only; broad paid-tier availability followed October 13 (not on the free tier). At $2 in / $10 out per million tokens, it sits in the mid 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 4 Argon 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 4 Argon 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.
See pricingFrequently asked questions
Is Gemini 4 Argon 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 4 Argon leans toward 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses 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 4 Argon 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 4 Argon is API-metered at $2/$10 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?
Effectively neither — 1M tokens vs 1M is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Gemini 4 Argon and Llama 4 Maverick together?
Yes — a multi-model platform like LumiChats gives you Gemini 4 Argon, 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 4 Argon or Llama 4 Maverick?
Gemini 4 Argon — released October 13, 2026, about 19 months after Llama 4 Maverick.
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.