gpt-oss-120b vs Reflection Beam 1.0
OpenAI · US | Reflection AI · US · Updated June 2026
Quick verdict
Pick gpt-oss-120b for self-hostable on a single 80gb h100 gpu via mxfp4 or configurable reasoning depth (low/medium/high). 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).
gpt-oss-120b (OpenAI) and Reflection Beam 1.0 (Reflection AI) are two of the models people most often weigh against each other in 2026. gpt-oss-120b is openAI's open-weight 117B-parameter MoE reasoning model (5.1B active) that runs on a single 80GB GPU and approaches o4-mini on reasoning, coding, and tool use. 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
- ▸Context window: Reflection Beam 1.0 holds 7.6× more — 1M tokens (~1,500 pages) vs 131K (~197 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Recency: Reflection Beam 1.0 is the newer model by about 15 months (released October 16, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | gpt-oss-120b | Reflection Beam 1.0 |
|---|---|---|
| Provider | OpenAI (US) | Reflection AI (US) |
| Released | August 5, 2025 | October 16, 2026 |
| Context window | 131K (~197 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 |
| Modalities | text, code | text, code |
| SWE-Bench Verified | 62.4% | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Self-hostable on a single 80GB H100 GPU via MXFP4
gpt-oss-120b
gpt-oss-120b lists self-hostable on a single 80GB H100 GPU via MXFP4 among its strengths; Reflection Beam 1.0 does not.
Configurable reasoning depth (low/medium/high)
gpt-oss-120b
Reflection Beam 1.0 is comparatively weak here — text-only input; some users report reasoning loops on long agent runs
Agentic tool use, function calling, and code execution
gpt-oss-120b
gpt-oss-120b lists agentic tool use, function calling, and code execution 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 7.6× more than gpt-oss-120b's 131K in a single prompt.
Reflection claims 3-4x less inference compute than rival models (vendor claim)
Reflection Beam 1.0
gpt-oss-120b is comparatively weak here — 131K context and 5.1B active params trail the largest frontier closed 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 7.6× larger than gpt-oss-120b's 131K, 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 self-hostable on a single 80gb h100 gpu via mxfp4
→ gpt-oss-120b
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.
gpt-oss-120b: where it fits
OpenAI's open-weight 117B-parameter MoE reasoning model (5.1B active) that runs on a single 80GB GPU and approaches o4-mini on reasoning, coding, and tool use. Released August 5, 2025 by OpenAI, it is built for self-hostable on a single 80GB H100 GPU via MXFP4, configurable reasoning depth (low/medium/high), agentic tool use, function calling, and code execution, and full chain-of-thought visibility for debugging.
Its trade-offs are real: text-only, no image, audio, or video input, and 131K context and 5.1B active params trail the largest frontier closed models. 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
gpt-oss-120b 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 — gpt-oss-120b for self-hostable on a single 80gb h100 gpu via mxfp4, 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 gpt-oss-120b 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 gpt-oss-120b 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, gpt-oss-120b leans toward self-hostable on a single 80gb h100 gpu via mxfp4 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, gpt-oss-120b 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 131K, about 7.6× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both gpt-oss-120b and Reflection Beam 1.0 together?
Yes — a multi-model platform like LumiChats gives you gpt-oss-120b, 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, gpt-oss-120b or Reflection Beam 1.0?
Reflection Beam 1.0 — released October 16, 2026, about 15 months after gpt-oss-120b.
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.