IBM Granite 4.1 vs Reflection Beam 1.0

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

Quick verdict

Pick IBM Granite 4.1 for enterprise-grade open weights - apache 2.0, iso 42001-certified, cryptographically signed or efficient hybrid mamba-2/transformer design - much lower memory and faster inference. 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).

IBM Granite 4.1 (IBM) and Reflection Beam 1.0 (Reflection AI) are two of the models people most often weigh against each other in 2026. IBM Granite 4.1 is iBM's most complete open-weight enterprise release - small, Apache-2.0, long-context and governance-friendly, built to deploy efficiently rather than top the leaderboard. 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. Their biggest split is context window, and the breakdown below shows exactly how that plays out for your workload.

Key differences at a glance

Side-by-side specs

SpecIBM Granite 4.1Reflection Beam 1.0
ProviderIBM (US) Reflection AI (US)
ReleasedApril 29, 2026 October 16, 2026
Context window512K (~768 pages) 1M tokens (~1,500 pages)
Price (in/out)Open weight (self-host / free) Open weight (self-host / free)
Open weight?Yes — self-hostable Yes — self-hostable
Modalitiestext, code text, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Enterprise-grade open weights - Apache 2.0, ISO 42001-certified, cryptographically signed

IBM Granite 4.1

IBM Granite 4.1 lists enterprise-grade open weights - Apache 2.0, ISO 42001-certified, cryptographically signed among its strengths; Reflection Beam 1.0 does not.

Efficient hybrid Mamba-2/transformer design - much lower memory and faster inference

IBM Granite 4.1

Reflection Beam 1.0 is comparatively weak here — self-hosting needs about 353 GB of GPU memory even at NVFP4, and kernels are CUDA-only for now

512K-token context on small, deployable dense models (3B/8B/30B)

IBM Granite 4.1

IBM Granite 4.1 lists 512K-token context on small, deployable dense models (3B/8B/30B) 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 2× more than IBM Granite 4.1's 512K in a single prompt.

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

Reflection Beam 1.0

IBM Granite 4.1 is comparatively weak here — best as a workhorse; reasoning-heavy tasks favor larger models

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 it carries the larger 1M tokens context.

Largest single-prompt input

Reflection Beam 1.0

Its 1M tokens window is about 2× larger than IBM Granite 4.1's 512K, fitting roughly 1,500 pages in one prompt.

Which should you pick?

Someone analysing very long documents or codebases

→ Reflection Beam 1.0

Larger 1M tokens window fits more in one prompt.

Anyone whose priority is enterprise-grade open weights - apache 2.0, iso 42001-certified, cryptographically signed

→ IBM Granite 4.1

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.

IBM Granite 4.1: where it fits

IBM's most complete open-weight enterprise release - small, Apache-2.0, long-context and governance-friendly, built to deploy efficiently rather than top the leaderboard. Released April 29, 2026 by IBM, it is built for enterprise-grade open weights - Apache 2.0, ISO 42001-certified, cryptographically signed, efficient hybrid Mamba-2/transformer design - much lower memory and faster inference, 512K-token context on small, deployable dense models (3B/8B/30B), and free to self-host; governance-friendly for on-prem and regulated deployments.

Its trade-offs are real: not a frontier-intelligence competitor - built for efficient deployment, not top benchmark scores, best as a workhorse; reasoning-heavy tasks favor larger models, instruct models are text-focused (vision and speech are separate family members), and efficiency and performance claims are IBM's own. As an open-weight model, its running cost is your own hardware rather than a per-token fee.

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

IBM Granite 4.1 and Reflection Beam 1.0 overlap enough that the right pick depends on your specific job. Reflection Beam 1.0 holds the larger context; and each leads in its own area — IBM Granite 4.1 for enterprise-grade open weights - apache 2.0, iso 42001-certified, cryptographically signed, Reflection Beam 1.0 for 501b-parameter sparse moe (about 23b active per token) with weights on hugging face under apache 2.0. Rather than crowning one, run the same hard task through both once and let the results decide.

Want both IBM Granite 4.1 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 IBM Granite 4.1 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, IBM Granite 4.1 leans toward enterprise-grade open weights - apache 2.0, iso 42001-certified, cryptographically signed 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, IBM Granite 4.1 or Reflection Beam 1.0?

They are priced almost identically, so cost will not decide between them.

Which has the bigger context window?

Reflection Beam 1.0 — 1M tokens vs 512K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both IBM Granite 4.1 and Reflection Beam 1.0 together?

Yes — a multi-model platform like LumiChats gives you IBM Granite 4.1, 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, IBM Granite 4.1 or Reflection Beam 1.0?

Reflection Beam 1.0 — released October 16, 2026, about 6 months after IBM Granite 4.1.

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.