Llama 4 Maverick vs Step 5 Preview

Meta · US  |  StepFun · China · Updated June 2026

Quick verdict

Pick Llama 4 Maverick for open weights, 1m context or strong image + text understanding. Pick Step 5 Preview for sparse moe architecture — 600b total parameters, only 27b active per token — built for long-horizon agentic work across large codebases and documents or 1m-token context window (64k max output) at $1/$2.70 per million tokens, among the cheapest frontier-scale context windows available. Choose Llama 4 Maverick if you need self-hosting or data privacy; Step 5 Preview if you want a managed API.

Llama 4 Maverick (Meta, US) and Step 5 Preview (StepFun, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Llama 4 Maverick is meta's open-weight 1M-context multimodal model for self-hosted deployments. Step 5 Preview is stepFun's September 20, 2026 flagship preview — a 600B-parameter sparse MoE model (27B active) with a 1M-token context window at $1/$2.70 per million tokens, open weights promised for mid-October. They diverge most on price and open vs. closed weights — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecLlama 4 MaverickStep 5 Preview
ProviderMeta (US) StepFun (China)
ReleasedApril 2025 September 20, 2026
Context window1M (~1,500 pages) 1M tokens (~1,500 pages)
Price (in/out)Open weight (self-host / free) $1/$2.7 per 1M tokens
Open weight?Yes — self-hostable No — API only
Modalitiestext, image, code text, image, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Open weights, 1M context

Llama 4 Maverick

Open weights make this possible at all — Step 5 Preview 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 Step 5 Preview is API-only.

Self-hostable

Llama 4 Maverick

Llama 4 Maverick lists self-hostable among its strengths; Step 5 Preview does not.

Sparse MoE architecture — 600B total parameters, only 27B active per token — built for long-horizon agentic work across large codebases and documents

Step 5 Preview

StepFun's September 20, 2026 flagship preview — a 600B-parameter sparse MoE model (27B active) with a 1M-token context window at $1/$2.70 per million tokens, open weights promised for mid-October — and it is the newer of the two.

1M-token context window (64K max output) at $1/$2.70 per million tokens, among the cheapest frontier-scale context windows available

Step 5 Preview

Llama 4 Maverick is comparatively weak here — trails closed frontier on reasoning

67.7% on DeepSWE v1.1, ahead of open-weight rivals Kimi K3 (67.5%) and GLM-5.3 (66.9%)

Step 5 Preview

Step 5 Preview lists 67.7% on DeepSWE v1.1, ahead of open-weight rivals Kimi K3 (67.5%) and GLM-5.3 (66.9%) 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 Step 5 Preview's $1/$2.7 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 Step 5 Preview, 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; Step 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 sparse moe architecture — 600b total parameters, only 27b active per token — built for long-horizon agentic work across large codebases and documents

→ Step 5 Preview

That is its strongest area.

An enterprise with regional data-residency rules

→ Llama 4 Maverick or Step 5 Preview

Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.

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.

Step 5 Preview: where it fits

StepFun's September 20, 2026 flagship preview — a 600B-parameter sparse MoE model (27B active) with a 1M-token context window at $1/$2.70 per million tokens, open weights promised for mid-October. Released September 20, 2026 by StepFun, it is built for sparse MoE architecture — 600B total parameters, only 27B active per token — built for long-horizon agentic work across large codebases and documents, 1M-token context window (64K max output) at $1/$2.70 per million tokens, among the cheapest frontier-scale context windows available, 67.7% on DeepSWE v1.1, ahead of open-weight rivals Kimi K3 (67.5%) and GLM-5.3 (66.9%), and scores 44 on the Artificial Analysis Intelligence Index, ranking 27th of 653 models tracked.

Its trade-offs: trails closed frontier models on coding — 67.7% on DeepSWE v1.1 vs GPT-6 Astra's 74.1% and Claude Opus 5's 74.0%, open weights were promised for October 15, 2026 but hadn't shipped as of this writing — API-only for now despite the open-weight roadmap, a 'Preview' release, not StepFun's finished flagship — positioning and pricing may change once weights ship, and stepCodeBench and DeepSWE figures are StepFun's own reported numbers, not yet independently reproduced at scale. At $1 in / $2.7 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. Step 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 Step 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.

See pricing

Frequently asked questions

Is Llama 4 Maverick or Step 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 Step 5 Preview leans toward sparse moe architecture — 600b total parameters, only 27b active per token — built for long-horizon agentic work across large codebases and documents, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Llama 4 Maverick or Step 5 Preview?

Llama 4 Maverick is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Step 5 Preview is API-metered at $1/$2.7 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 Llama 4 Maverick and Step 5 Preview together?

Yes — a multi-model platform like LumiChats gives you Llama 4 Maverick, Step 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 Step 5 Preview?

Step 5 Preview — released September 20, 2026, about 18 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.