Claude Haiku 5.5 vs Llama 4 Maverick
Anthropic · US | Meta · US · Updated June 2026
Quick verdict
Pick Claude Haiku 5.5 for 5x the context window of haiku 4.5 (1m vs 200k tokens) and double the max output (128k vs 64k) or first haiku-class model with an adjustable 'effort' setting to trade off cost, latency, and intelligence. 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; Claude Haiku 5.5 if you want a managed API.
Claude Haiku 5.5 (Anthropic) and Llama 4 Maverick (Meta) are two of the models people most often weigh against each other in 2026. Claude Haiku 5.5 is anthropic's October 7, 2026 small-model refresh — a 1M-token context window (up from 200K) at roughly 75% lower average cost than Haiku 4.5, priced at $0.10/$0.50 per million tokens. 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 Claude Haiku 5.5 is API-metered at $0.1/$0.5 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
- ▸Context window: both advertise 1M tokens (~1,500 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
- ▸Recency: Claude Haiku 5.5 is the newer model by about 18 months (released October 7, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | Claude Haiku 5.5 | Llama 4 Maverick |
|---|---|---|
| Provider | Anthropic (US) | Meta (US) |
| Released | October 7, 2026 | April 2025 |
| Context window | 1M tokens (~1,500 pages) | 1M (~1,500 pages) |
| Price (in/out) | $0.1/$0.5 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
5x the context window of Haiku 4.5 (1M vs 200K tokens) and double the max output (128K vs 64K)
Claude Haiku 5.5
Anthropic's October 7, 2026 small-model refresh — a 1M-token context window (up from 200K) at roughly 75% lower average cost than Haiku 4.5, priced at $0.10/$0.50 per million tokens — and it is the newer of the two.
First Haiku-class model with an adjustable 'effort' setting to trade off cost, latency, and intelligence
Claude Haiku 5.5
Claude Haiku 5.5 lists first Haiku-class model with an adjustable 'effort' setting to trade off cost, latency, and intelligence among its strengths; Llama 4 Maverick does not.
Anthropic's cheapest small model yet — runs about 75% cheaper on average than Haiku 4.5
Claude Haiku 5.5
Claude Haiku 5.5 lists anthropic's cheapest small model yet — runs about 75% cheaper on average than Haiku 4.5 among its strengths; Llama 4 Maverick does not.
Open weights, 1M context
Llama 4 Maverick
Open weights make this possible at all — Claude Haiku 5.5 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 Claude Haiku 5.5 is API-only.
Self-hostable
Llama 4 Maverick
Llama 4 Maverick lists self-hostable among its strengths; Claude Haiku 5.5 does not.
Lowest cost at scale
Llama 4 Maverick
Its weights are open, so at volume you pay for your own hardware instead of Claude Haiku 5.5's $0.1/$0.5 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 Claude Haiku 5.5, 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; Claude Haiku 5.5 is API-only.
Anyone whose priority is 5x the context window of haiku 4.5 (1m vs 200k tokens) and double the max output (128k vs 64k)
→ Claude Haiku 5.5
It is specifically built for that.
Anyone whose priority is open weights, 1m context
→ Llama 4 Maverick
That is its strongest area.
Claude Haiku 5.5: where it fits
Anthropic's October 7, 2026 small-model refresh — a 1M-token context window (up from 200K) at roughly 75% lower average cost than Haiku 4.5, priced at $0.10/$0.50 per million tokens. Released October 7, 2026 by Anthropic, it is built for 5x the context window of Haiku 4.5 (1M vs 200K tokens) and double the max output (128K vs 64K), first Haiku-class model with an adjustable 'effort' setting to trade off cost, latency, and intelligence, anthropic's cheapest small model yet — runs about 75% cheaper on average than Haiku 4.5, and over-refuses benign requests only 0.17% of the time on the API, down from Haiku 4.5's 0.44%.
Its trade-offs are real: pricing jumps roughly 5x beyond 100K-token prompts ($0.50/$2.50 per million vs the headline $0.10/$0.50), extended thinking can no longer be fully disabled — only dialed between low, medium, and high effort, and as the smallest tier, trails both Sonnet 5.5 and Opus 5.5 on hard reasoning and open-ended work. At $0.1 in / $0.5 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. Claude Haiku 5.5 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 Claude Haiku 5.5 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 Claude Haiku 5.5 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, Claude Haiku 5.5 leans toward 5x the context window of haiku 4.5 (1m vs 200k tokens) and double the max output (128k vs 64k) while Llama 4 Maverick leans toward open weights, 1m context, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Claude Haiku 5.5 or Llama 4 Maverick?
Llama 4 Maverick is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Claude Haiku 5.5 is API-metered at $0.1/$0.5 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 tokens (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Claude Haiku 5.5 and Llama 4 Maverick together?
Yes — a multi-model platform like LumiChats gives you Claude Haiku 5.5, 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, Claude Haiku 5.5 or Llama 4 Maverick?
Claude Haiku 5.5 — released October 7, 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.