Claude Haiku 4.5 vs Reflection Beam 1.0

Anthropic · US  |  Reflection AI · US · Updated June 2026

Quick verdict

Pick Claude Haiku 4.5 for fastest claude model or near-frontier coding for its tier — 73.3% on swe-bench verified. Pick Reflection Beam 1.0 for 501b-parameter sparse moe (about 23b active per token) with weights on hugging face under apache 2.0 or reflection claims 3-4x less inference compute than rival models (vendor claim). Choose Reflection Beam 1.0 if you need self-hosting or data privacy; Claude Haiku 4.5 if you want a managed API.

Claude Haiku 4.5 (Anthropic) and Reflection Beam 1.0 (Reflection AI) are two of the models people most often weigh against each other in 2026. Claude Haiku 4.5 is anthropic's fastest, most compact model — built for speed and volume. Reflection Beam 1.0 is reflection AI's first open-weight model - a 501B Apache 2.0 MoE announced October 5 with weights published October 16, 2026, pitched as a US alternative to Chinese open models. 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

Side-by-side specs

SpecClaude Haiku 4.5Reflection Beam 1.0
ProviderAnthropic (US) Reflection AI (US)
ReleasedOctober 15, 2025 October 16, 2026
Context window200K (~300 pages) 1M tokens (~1,500 pages)
Price (in/out)$1/$5 per 1M tokens Open weight (self-host / free)
Open weight?No — API only Yes — self-hostable
Modalitiestext, image, code text, code
SWE-Bench Verified73.3% Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Fastest Claude model

Claude Haiku 4.5

Reflection Beam 1.0 is comparatively weak here — headline benchmarks come from Reflection's own model card; independent evaluation was limited at launch

Near-frontier coding for its tier — 73.3% on SWE-Bench Verified

Claude Haiku 4.5

Claude Haiku 4.5 lists near-frontier coding for its tier — 73.3% on SWE-Bench Verified among its strengths; Reflection Beam 1.0 does not.

Low-latency, high-volume API calls

Claude Haiku 4.5

Claude Haiku 4.5 lists low-latency, high-volume API calls among its strengths; Reflection Beam 1.0 does not.

501B-parameter sparse MoE (about 23B active per token) with weights on Hugging Face under Apache 2.0

Reflection Beam 1.0

Its 1M tokens window holds about 5× more than Claude Haiku 4.5's 200K in a single prompt.

Reflection claims 3-4x less inference compute than rival models (vendor claim)

Reflection Beam 1.0

Reflection AI's first open-weight model - a 501B Apache 2.0 MoE announced October 5 with weights published October 16, 2026, pitched as a US alternative to Chinese open models — and it carries the larger 1M tokens context.

Fast and cheap to run: Artificial Analysis measured about 385 output tokens/sec and $0.12 per index task

Reflection Beam 1.0

Reflection AI's first open-weight model - a 501B Apache 2.0 MoE announced October 5 with weights published October 16, 2026, pitched as a US alternative to Chinese open models — and its weights are open while Claude Haiku 4.5 is API-only.

Lowest cost at scale

Reflection Beam 1.0

Its weights are open, so at volume you pay for your own hardware instead of Claude Haiku 4.5's $1/$5 per 1M tokens.

Largest single-prompt input

Reflection Beam 1.0

Its 1M tokens window is about 5× larger than Claude Haiku 4.5's 200K, fitting roughly 1,500 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

→ Reflection Beam 1.0

At Open weight (self-host / free) it undercuts Claude Haiku 4.5, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

→ Reflection Beam 1.0

Larger 1M tokens window fits more in one prompt.

A team with data-privacy or self-hosting needs

→ Reflection Beam 1.0

Open weights let you run it on your own hardware; Claude Haiku 4.5 is API-only.

Anyone whose priority is fastest claude model

→ Claude Haiku 4.5

It is specifically built for that.

Anyone whose priority is 501b-parameter sparse moe (about 23b active per token) with weights on hugging face under apache 2.0

→ Reflection Beam 1.0

That is its strongest area.

Claude Haiku 4.5: where it fits

Anthropic's fastest, most compact model — built for speed and volume. Released October 15, 2025 by Anthropic, it is built for fastest Claude model, near-frontier coding for its tier — 73.3% on SWE-Bench Verified, low-latency, high-volume API calls, and cheapest Claude tier at $1/$5.

Its trade-offs are real: smallest context in the family (200K), and not for deep reasoning. At $1 in / $5 out per million tokens, it sits in the budget price band.

Reflection Beam 1.0: where it fits

Reflection AI's first open-weight model - a 501B Apache 2.0 MoE announced October 5 with weights published October 16, 2026, pitched as a US alternative to Chinese open models. Released October 16, 2026 by Reflection AI, it is built for 501B-parameter sparse MoE (about 23B active per token) with weights on Hugging Face under Apache 2.0, reflection claims 3-4x less inference compute than rival models (vendor claim), fast and cheap to run: Artificial Analysis measured about 385 output tokens/sec and $0.12 per index task, and third-party hosts list it from $0.25/$1.00 per million tokens (Nebius, as of Oct 27); Reflection's own API is in free beta.

Its trade-offs: artificial Analysis Intelligence Index of 30, well below DeepSeek V4.1 Flash and GLM-5.3 (45), text-only input; some users report reasoning loops on long agent runs, self-hosting needs about 353 GB of GPU memory even at NVFP4, and kernels are CUDA-only for now, and headline benchmarks come from Reflection's own model card; independent evaluation was limited at launch. 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. Reflection Beam 1.0 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Claude Haiku 4.5 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 4.5 and Reflection Beam 1.0 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 Claude Haiku 4.5 or Reflection Beam 1.0 better for coding?

Public SWE-Bench figures are not available for Reflection Beam 1.0, so the honest test is your own repository — run an identical real bug through both. By design, Claude Haiku 4.5 leans toward fastest claude model while Reflection Beam 1.0 leans toward 501b-parameter sparse moe (about 23b active per token) with weights on hugging face under apache 2.0, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Claude Haiku 4.5 or Reflection Beam 1.0?

Reflection Beam 1.0 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Claude Haiku 4.5 is API-metered at $1/$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?

Reflection Beam 1.0 — 1M tokens vs 200K, about 5× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both Claude Haiku 4.5 and Reflection Beam 1.0 together?

Yes — a multi-model platform like LumiChats gives you Claude Haiku 4.5, Reflection Beam 1.0 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 4.5 or Reflection Beam 1.0?

Reflection Beam 1.0 — released October 16, 2026, about 12 months after Claude Haiku 4.5.

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.