Pick Grok 4.7 for 2.1 trillion parameters, up 40% from grok 4.6's 1.5 trillion, at the same $2/$6 per million token price or deepswe v1.1 (high effort): 71.0%, up from grok 4.6's 65.2%; cursorbench 4.0: 46.3%, up from 40.4%. 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; Grok 4.7 if you want a managed API.
Grok 4.7 (xAI) and Llama 4 Maverick (Meta) are two of the models people most often weigh against each other in 2026. Grok 4.7 is xAI's September 21, 2026 update — a 2.1T-parameter model trained partly on SpaceX hardware data, same price as Grok 4.6 but stronger on coding benchmarks. 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
Cost model: Llama 4 Maverick ships open weights you can self-host (hardware cost only, no per-token fee), while Grok 4.7 is API-metered at $2/$6 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
Context window: Llama 4 Maverick holds 2× more — 1M (~1,500 pages) vs 500K tokens (~750 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: Grok 4.7 is the newer model by about 18 months (released September 21, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Grok 4.7
Llama 4 Maverick
Provider
xAI (US)
Meta (US)
Released
September 21, 2026
April 2025
Context window
500K tokens (~750 pages)
1M (~1,500 pages)
Price (in/out)
$2/$6 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
2.1 trillion parameters, up 40% from Grok 4.6's 1.5 trillion, at the same $2/$6 per million token price: Grok 4.7 — XAI's September 21, 2026 update — a 2.1T-parameter model trained partly on SpaceX hardware data, same price as Grok 4.6 but stronger on coding benchmarks — and it is the newer of the two.
DeepSWE v1.1 (high effort): 71.0%, up from Grok 4.6's 65.2%; CursorBench 4.0: 46.3%, up from 40.4%: Grok 4.7 — Grok 4.7 lists deepSWE v1.1 (high effort): 71.0%, up from Grok 4.6's 65.2%; CursorBench 4.0: 46.3%, up from 40.4% among its strengths; Llama 4 Maverick does not.
Trained with supplemental SpaceX data (Starlink telemetry, manufacturing records, engineering failure logs) — xAI says this improves reasoning about hardware and physical systems: Grok 4.7 — Llama 4 Maverick is comparatively weak here — needs serious hardware to self-host
Open weights, 1M context: Llama 4 Maverick — Its 1M window holds about 2× more than Grok 4.7's 500K tokens in a single prompt.
Strong image + text understanding: Llama 4 Maverick — Meta's open-weight 1M-context multimodal model for self-hosted deployments — and it carries the larger 1M context.
Self-hostable: Llama 4 Maverick — Open weights make this possible at all — Grok 4.7 is API-only, so it cannot leave the vendor's servers.
Lowest cost at scale: Llama 4 Maverick — Its weights are open, so at volume you pay for your own hardware instead of Grok 4.7's $2/$6 per 1M tokens.
Largest single-prompt input: Llama 4 Maverick — Its 1M window is about 2× larger than Grok 4.7's 500K tokens, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Llama 4 Maverick — At Open weight (self-host / free) it undercuts Grok 4.7, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Llama 4 Maverick — Larger 1M 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; Grok 4.7 is API-only.
Anyone whose priority is 2.1 trillion parameters, up 40% from grok 4.6's 1.5 trillion, at the same $2/$6 per million token price: Grok 4.7 — It is specifically built for that.
Anyone whose priority is open weights, 1m context: Llama 4 Maverick — That is its strongest area.
Grok 4.7: where it fits
XAI's September 21, 2026 update — a 2.1T-parameter model trained partly on SpaceX hardware data, same price as Grok 4.6 but stronger on coding benchmarks. Released September 21, 2026 by xAI, it is built for 2.1 trillion parameters, up 40% from Grok 4.6's 1.5 trillion, at the same $2/$6 per million token price, deepSWE v1.1 (high effort): 71.0%, up from Grok 4.6's 65.2%; CursorBench 4.0: 46.3%, up from 40.4%, trained with supplemental SpaceX data (Starlink telemetry, manufacturing records, engineering failure logs) — xAI says this improves reasoning about hardware and physical systems, and xAI's strongest safety guardrails to date, per the company.
Its trade-offs are real: release was delayed at least five times since late July 2026 before shipping, 500K context window trails several rivals now sitting at 1M+, and reviewers note it arrives "late to the AI frontier party" against GPT-6 Astra, Claude Fable 5.1 and Opus 5.5, all shipped in the weeks just before it. At $2 in / $6 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. Grok 4.7 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 Grok 4.7 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, Grok 4.7 leans toward 2.1 trillion parameters, up 40% from grok 4.6's 1.5 trillion, at the same $2/$6 per million token price while Llama 4 Maverick leans toward open weights, 1m context, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Grok 4.7 or Llama 4 Maverick?
Llama 4 Maverick is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Grok 4.7 is API-metered at $2/$6 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 Maverick — 1M vs 500K tokens, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Grok 4.7 and Llama 4 Maverick together?
Yes — a multi-model platform like LumiChats gives you Grok 4.7, 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, Grok 4.7 or Llama 4 Maverick?
Grok 4.7 — released September 21, 2026, about 18 months after Llama 4 Maverick.
Grok 4.7 vs Llama 4 Maverick
xAI · US | Meta · US · Updated June 2026
Quick verdict
Pick Grok 4.7 for 2.1 trillion parameters, up 40% from grok 4.6's 1.5 trillion, at the same $2/$6 per million token price or deepswe v1.1 (high effort): 71.0%, up from grok 4.6's 65.2%; cursorbench 4.0: 46.3%, up from 40.4%. 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; Grok 4.7 if you want a managed API.
Grok 4.7 (xAI) and Llama 4 Maverick (Meta) are two of the models people most often weigh against each other in 2026. Grok 4.7 is xAI's September 21, 2026 update — a 2.1T-parameter model trained partly on SpaceX hardware data, same price as Grok 4.6 but stronger on coding benchmarks. 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 Grok 4.7 is API-metered at $2/$6 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
▸Context window: Llama 4 Maverick holds 2× more — 1M (~1,500 pages) vs 500K tokens (~750 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Grok 4.7 is the newer model by about 18 months (released September 21, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Grok 4.7
Llama 4 Maverick
Provider
xAI (US)
Meta (US)
Released
September 21, 2026
April 2025
Context window
500K tokens (~750 pages)
1M (~1,500 pages)
Price (in/out)
$2/$6 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
2.1 trillion parameters, up 40% from Grok 4.6's 1.5 trillion, at the same $2/$6 per million token price
Grok 4.7
XAI's September 21, 2026 update — a 2.1T-parameter model trained partly on SpaceX hardware data, same price as Grok 4.6 but stronger on coding benchmarks — and it is the newer of the two.
DeepSWE v1.1 (high effort): 71.0%, up from Grok 4.6's 65.2%; CursorBench 4.0: 46.3%, up from 40.4%
Grok 4.7
Grok 4.7 lists deepSWE v1.1 (high effort): 71.0%, up from Grok 4.6's 65.2%; CursorBench 4.0: 46.3%, up from 40.4% among its strengths; Llama 4 Maverick does not.
Trained with supplemental SpaceX data (Starlink telemetry, manufacturing records, engineering failure logs) — xAI says this improves reasoning about hardware and physical systems
Grok 4.7
Llama 4 Maverick is comparatively weak here — needs serious hardware to self-host
Open weights, 1M context
Llama 4 Maverick
Its 1M window holds about 2× more than Grok 4.7's 500K tokens in a single prompt.
Strong image + text understanding
Llama 4 Maverick
Meta's open-weight 1M-context multimodal model for self-hosted deployments — and it carries the larger 1M context.
Self-hostable
Llama 4 Maverick
Open weights make this possible at all — Grok 4.7 is API-only, so it cannot leave the vendor's servers.
Lowest cost at scale
Llama 4 Maverick
Its weights are open, so at volume you pay for your own hardware instead of Grok 4.7's $2/$6 per 1M tokens.
Largest single-prompt input
Llama 4 Maverick
Its 1M window is about 2× larger than Grok 4.7's 500K tokens, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Llama 4 Maverick
At Open weight (self-host / free) it undercuts Grok 4.7, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Llama 4 Maverick
Larger 1M 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; Grok 4.7 is API-only.
Anyone whose priority is 2.1 trillion parameters, up 40% from grok 4.6's 1.5 trillion, at the same $2/$6 per million token price
→ Grok 4.7
It is specifically built for that.
Anyone whose priority is open weights, 1m context
→ Llama 4 Maverick
That is its strongest area.
Grok 4.7: where it fits
XAI's September 21, 2026 update — a 2.1T-parameter model trained partly on SpaceX hardware data, same price as Grok 4.6 but stronger on coding benchmarks. Released September 21, 2026 by xAI, it is built for 2.1 trillion parameters, up 40% from Grok 4.6's 1.5 trillion, at the same $2/$6 per million token price, deepSWE v1.1 (high effort): 71.0%, up from Grok 4.6's 65.2%; CursorBench 4.0: 46.3%, up from 40.4%, trained with supplemental SpaceX data (Starlink telemetry, manufacturing records, engineering failure logs) — xAI says this improves reasoning about hardware and physical systems, and xAI's strongest safety guardrails to date, per the company.
Its trade-offs are real: release was delayed at least five times since late July 2026 before shipping, 500K context window trails several rivals now sitting at 1M+, and reviewers note it arrives "late to the AI frontier party" against GPT-6 Astra, Claude Fable 5.1 and Opus 5.5, all shipped in the weeks just before it. At $2 in / $6 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. Grok 4.7 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 Grok 4.7 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 Grok 4.7 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, Grok 4.7 leans toward 2.1 trillion parameters, up 40% from grok 4.6's 1.5 trillion, at the same $2/$6 per million token price while Llama 4 Maverick leans toward open weights, 1m context, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Grok 4.7 or Llama 4 Maverick?
Llama 4 Maverick is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Grok 4.7 is API-metered at $2/$6 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 Maverick — 1M vs 500K tokens, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Grok 4.7 and Llama 4 Maverick together?
Yes — a multi-model platform like LumiChats gives you Grok 4.7, 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, Grok 4.7 or Llama 4 Maverick?
Grok 4.7 — released September 21, 2026, about 18 months 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.