Pick Llama 4 Maverick for open weights, 1m context or strong image + text understanding. Pick Mercury 2.5 Preview for very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation or coding accuracy (95.7%, 91st percentile among cost-optimized models). Choose Llama 4 Maverick if you need self-hosting or data privacy; Mercury 2.5 Preview if you want a managed API.
Llama 4 Maverick (Meta) and Mercury 2.5 Preview (Inception Labs) are two of the models people most often weigh against each other in 2026. Llama 4 Maverick is meta's open-weight 1M-context multimodal model for self-hosted deployments. Mercury 2.5 Preview is inception Labs' August 31, 2026 diffusion-based language model preview, refining tokens in parallel for roughly 10x the throughput of comparable autoregressive models at cost-optimized-tier quality. 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 Mercury 2.5 Preview is API-metered at $0.04/$0.15 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 3.8× more — 1M (~1,500 pages) vs 260K tokens (~390 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: Mercury 2.5 Preview is the newer model by about 17 months (released August 31, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Llama 4 Maverick
Mercury 2.5 Preview
Provider
Meta (US)
Inception Labs (US)
Released
April 2025
August 31, 2026
Context window
1M (~1,500 pages)
260K tokens (~390 pages)
Price (in/out)
Open weight (self-host / free)
$0.04/$0.15 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, image, code
text
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Open weights, 1M context: Llama 4 Maverick — Its 1M window holds about 3.8× more than Mercury 2.5 Preview's 260K 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 — Mercury 2.5 Preview is API-only, so it cannot leave the vendor's servers.
Very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation: Mercury 2.5 Preview — Inception Labs' August 31, 2026 diffusion-based language model preview, refining tokens in parallel for roughly 10x the throughput of comparable autoregressive models at cost-optimized-tier quality — and it is the newer of the two.
Coding accuracy (95.7%, 91st percentile among cost-optimized models): Mercury 2.5 Preview — Mercury 2.5 Preview lists coding accuracy (95.7%, 91st percentile among cost-optimized models) among its strengths; Llama 4 Maverick does not.
Mathematics accuracy (97.0%, 97th percentile): Mercury 2.5 Preview — Mercury 2.5 Preview lists mathematics accuracy (97.0%, 97th percentile) among its strengths; Llama 4 Maverick does not.
Lowest cost at scale: Llama 4 Maverick — Its weights are open, so at volume you pay for your own hardware instead of Mercury 2.5 Preview's $0.04/$0.15 per 1M tokens.
Largest single-prompt input: Llama 4 Maverick — Its 1M window is about 3.8× larger than Mercury 2.5 Preview's 260K 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 Mercury 2.5 Preview, 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; Mercury 2.5 Preview is API-only.
Anyone whose priority is open weights, 1m context: Llama 4 Maverick — It is specifically built for that.
Anyone whose priority is very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation: Mercury 2.5 Preview — That is its strongest area.
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 are real: 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.
Mercury 2.5 Preview: where it fits
Inception Labs' August 31, 2026 diffusion-based language model preview, refining tokens in parallel for roughly 10x the throughput of comparable autoregressive models at cost-optimized-tier quality. Released August 31, 2026 by Inception Labs, it is built for very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation, coding accuracy (95.7%, 91st percentile among cost-optimized models), mathematics accuracy (97.0%, 97th percentile), and tunable reasoning levels with parallel tool calls and schema-aligned JSON output.
Its trade-offs: 260K context window is far shorter than frontier 1M-token models, no vision or audio modalities, positioned only against cost-optimized models (GPT-5.6 Luna Low, Gemini 3.5 Flash-Lite), not frontier-class, and current $0.04/$0.15 pricing includes a limited-time launch promotion on OpenRouter (list price is $0.20/$0.75, may rise after Sept 8, 2026). At $0.04 in / $0.15 out per million tokens, it sits in the budget price band.
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. Mercury 2.5 Preview 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 Llama 4 Maverick or Mercury 2.5 Preview 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, Llama 4 Maverick leans toward open weights, 1m context while Mercury 2.5 Preview leans toward very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Llama 4 Maverick or Mercury 2.5 Preview?
Llama 4 Maverick is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Mercury 2.5 Preview is API-metered at $0.04/$0.15 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 260K tokens, about 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Llama 4 Maverick and Mercury 2.5 Preview together?
Yes — a multi-model platform like LumiChats gives you Llama 4 Maverick, Mercury 2.5 Preview 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, Llama 4 Maverick or Mercury 2.5 Preview?
Mercury 2.5 Preview — released August 31, 2026, about 17 months after Llama 4 Maverick.
Llama 4 Maverick vs Mercury 2.5 Preview
Meta · US | Inception Labs · US · Updated June 2026
Quick verdict
Pick Llama 4 Maverick for open weights, 1m context or strong image + text understanding. Pick Mercury 2.5 Preview for very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation or coding accuracy (95.7%, 91st percentile among cost-optimized models). Choose Llama 4 Maverick if you need self-hosting or data privacy; Mercury 2.5 Preview if you want a managed API.
Llama 4 Maverick (Meta) and Mercury 2.5 Preview (Inception Labs) are two of the models people most often weigh against each other in 2026. Llama 4 Maverick is meta's open-weight 1M-context multimodal model for self-hosted deployments. Mercury 2.5 Preview is inception Labs' August 31, 2026 diffusion-based language model preview, refining tokens in parallel for roughly 10x the throughput of comparable autoregressive models at cost-optimized-tier quality. 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 Mercury 2.5 Preview is API-metered at $0.04/$0.15 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 3.8× more — 1M (~1,500 pages) vs 260K tokens (~390 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Mercury 2.5 Preview is the newer model by about 17 months (released August 31, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Llama 4 Maverick
Mercury 2.5 Preview
Provider
Meta (US)
Inception Labs (US)
Released
April 2025
August 31, 2026
Context window
1M (~1,500 pages)
260K tokens (~390 pages)
Price (in/out)
Open weight (self-host / free)
$0.04/$0.15 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, image, code
text
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Open weights, 1M context
Llama 4 Maverick
Its 1M window holds about 3.8× more than Mercury 2.5 Preview's 260K 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 — Mercury 2.5 Preview is API-only, so it cannot leave the vendor's servers.
Very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation
Mercury 2.5 Preview
Inception Labs' August 31, 2026 diffusion-based language model preview, refining tokens in parallel for roughly 10x the throughput of comparable autoregressive models at cost-optimized-tier quality — and it is the newer of the two.
Coding accuracy (95.7%, 91st percentile among cost-optimized models)
Mercury 2.5 Preview
Mercury 2.5 Preview lists coding accuracy (95.7%, 91st percentile among cost-optimized models) among its strengths; Llama 4 Maverick does not.
Mathematics accuracy (97.0%, 97th percentile)
Mercury 2.5 Preview
Mercury 2.5 Preview lists mathematics accuracy (97.0%, 97th percentile) among its strengths; Llama 4 Maverick does not.
Lowest cost at scale
Llama 4 Maverick
Its weights are open, so at volume you pay for your own hardware instead of Mercury 2.5 Preview's $0.04/$0.15 per 1M tokens.
Largest single-prompt input
Llama 4 Maverick
Its 1M window is about 3.8× larger than Mercury 2.5 Preview's 260K 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 Mercury 2.5 Preview, 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; Mercury 2.5 Preview is API-only.
Anyone whose priority is open weights, 1m context
→ Llama 4 Maverick
It is specifically built for that.
Anyone whose priority is very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation
→ Mercury 2.5 Preview
That is its strongest area.
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 are real: 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.
Mercury 2.5 Preview: where it fits
Inception Labs' August 31, 2026 diffusion-based language model preview, refining tokens in parallel for roughly 10x the throughput of comparable autoregressive models at cost-optimized-tier quality. Released August 31, 2026 by Inception Labs, it is built for very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation, coding accuracy (95.7%, 91st percentile among cost-optimized models), mathematics accuracy (97.0%, 97th percentile), and tunable reasoning levels with parallel tool calls and schema-aligned JSON output.
Its trade-offs: 260K context window is far shorter than frontier 1M-token models, no vision or audio modalities, positioned only against cost-optimized models (GPT-5.6 Luna Low, Gemini 3.5 Flash-Lite), not frontier-class, and current $0.04/$0.15 pricing includes a limited-time launch promotion on OpenRouter (list price is $0.20/$0.75, may rise after Sept 8, 2026). At $0.04 in / $0.15 out per million tokens, it sits in the budget price band.
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. Mercury 2.5 Preview 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 Llama 4 Maverick and Mercury 2.5 Preview 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 Llama 4 Maverick or Mercury 2.5 Preview 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, Llama 4 Maverick leans toward open weights, 1m context while Mercury 2.5 Preview leans toward very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Llama 4 Maverick or Mercury 2.5 Preview?
Llama 4 Maverick is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Mercury 2.5 Preview is API-metered at $0.04/$0.15 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 260K tokens, about 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Llama 4 Maverick and Mercury 2.5 Preview together?
Yes — a multi-model platform like LumiChats gives you Llama 4 Maverick, Mercury 2.5 Preview 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, Llama 4 Maverick or Mercury 2.5 Preview?
Mercury 2.5 Preview — released August 31, 2026, about 17 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.