Muse Wonder 1.0 vs Reflection Beam 1.0
Meta · US | Reflection AI · US · Updated June 2026
Quick verdict
Pick Muse Wonder 1.0 for meta superintelligence labs' first frontier-class reasoning model — a new line distinct from the agentic-coding-focused muse spark family or artificial analysis intelligence index score of 52 (a gated 'wonder full' subscription variant scores 56, tied with claude sonnet 5.5, behind only opus 5.5 at 58). 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; Muse Wonder 1.0 if you want a managed API.
Muse Wonder 1.0 (Meta) and Reflection Beam 1.0 (Reflection AI) are two of the models people most often weigh against each other in 2026. Muse Wonder 1.0 is meta Superintelligence Labs' October 12, 2026 debut frontier model — distinct from Muse Spark, scoring 52 on the independent Artificial Analysis Index with a 1M-token, four-modality context window. 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
- ▸Cost model: Reflection Beam 1.0 ships open weights you can self-host (hardware cost only, no per-token fee), while Muse Wonder 1.0 is API-metered at $4/$20 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
- ▸Context window: 1M tokens vs 1M tokens — within a few percent of each other, so treat this as a tie and test on your own long inputs, since usable recall varies by model.
Side-by-side specs
| Spec | Muse Wonder 1.0 | Reflection Beam 1.0 |
|---|---|---|
| Provider | Meta (US) | Reflection AI (US) |
| Released | October 12, 2026 | October 16, 2026 |
| Context window | 1M tokens (~1,573 pages) | 1M tokens (~1,500 pages) |
| Price (in/out) | $4/$20 per 1M tokens | Open weight (self-host / free) |
| Open weight? | No — API only | Yes — self-hostable |
| Modalities | text, image, video, audio | text, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Meta Superintelligence Labs' first frontier-class reasoning model — a new line distinct from the agentic-coding-focused Muse Spark family
Muse Wonder 1.0
Reflection Beam 1.0 is comparatively weak here — text-only input; some users report reasoning loops on long agent runs
Artificial Analysis Intelligence Index score of 52 (a gated 'Wonder Full' subscription variant scores 56, tied with Claude Sonnet 5.5, behind only Opus 5.5 at 58)
Muse Wonder 1.0
Reflection Beam 1.0 is comparatively weak here — artificial Analysis Intelligence Index of 30, well below DeepSeek V4.1 Flash and GLM-5.3 (45)
78.8% on DeepSWE v1.1 long-horizon coding, ahead of both Claude Opus 5.5 (74.2%) and GPT-6 Astra (74.1%)
Muse Wonder 1.0
Muse Wonder 1.0 lists 78.8% on DeepSWE v1.1 long-horizon coding, ahead of both Claude Opus 5.5 (74.2%) and GPT-6 Astra (74.1%) 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
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 Muse Wonder 1.0 is API-only.
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 is the newer of the two.
Fast and cheap to run: Artificial Analysis measured about 385 output tokens/sec and $0.12 per index task
Reflection Beam 1.0
Reflection Beam 1.0 lists fast and cheap to run: Artificial Analysis measured about 385 output tokens/sec and $0.12 per index task among its strengths; Muse Wonder 1.0 does not.
Lowest cost at scale
Reflection Beam 1.0
Its weights are open, so at volume you pay for your own hardware instead of Muse Wonder 1.0's $4/$20 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Reflection Beam 1.0
At Open weight (self-host / free) it undercuts Muse Wonder 1.0, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Muse Wonder 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; Muse Wonder 1.0 is API-only.
Anyone whose priority is meta superintelligence labs' first frontier-class reasoning model — a new line distinct from the agentic-coding-focused muse spark family
→ Muse Wonder 1.0
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.
Muse Wonder 1.0: where it fits
Meta Superintelligence Labs' October 12, 2026 debut frontier model — distinct from Muse Spark, scoring 52 on the independent Artificial Analysis Index with a 1M-token, four-modality context window. Released October 12, 2026 by Meta, it is built for meta Superintelligence Labs' first frontier-class reasoning model — a new line distinct from the agentic-coding-focused Muse Spark family, artificial Analysis Intelligence Index score of 52 (a gated 'Wonder Full' subscription variant scores 56, tied with Claude Sonnet 5.5, behind only Opus 5.5 at 58), 78.8% on DeepSWE v1.1 long-horizon coding, ahead of both Claude Opus 5.5 (74.2%) and GPT-6 Astra (74.1%), and accepts text, image, video, audio, and PDF input against a 1,048,576-token context window.
Its trade-offs are real: the stronger 'Wonder Full' variant is gated behind $50/$200-per-month Muse Code subscriptions, with no public API or independently verifiable pricing, meta's full safety report had not been published as of launch, first release in a brand-new model family — no track record yet next to Meta's more established Muse Spark line, and benchmark comparisons against Opus 5.5 and GPT-6 Astra are Meta's own self-reported figures. At $4 in / $20 out per million tokens, it sits in the mid 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. Muse Wonder 1.0 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 Muse Wonder 1.0 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 pricingFrequently asked questions
Is Muse Wonder 1.0 or Reflection Beam 1.0 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, Muse Wonder 1.0 leans toward meta superintelligence labs' first frontier-class reasoning model — a new line distinct from the agentic-coding-focused muse spark family 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, Muse Wonder 1.0 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 Muse Wonder 1.0 is API-metered at $4/$20 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?
Effectively neither — 1M tokens vs 1M tokens is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Muse Wonder 1.0 and Reflection Beam 1.0 together?
Yes — a multi-model platform like LumiChats gives you Muse Wonder 1.0, 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, Muse Wonder 1.0 or Reflection Beam 1.0?
Reflection Beam 1.0 — released October 16, 2026, about 4 days after Muse Wonder 1.0.
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.