GPT-5.6 Terra vs Reflection Beam 1.0

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

Quick verdict

Pick GPT-5.6 Terra for balanced everyday work at roughly half of sol's price or competitive with gpt-5.5 quality at about 2x lower cost. 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; GPT-5.6 Terra if you want a managed API.

GPT-5.6 Terra (OpenAI) and Reflection Beam 1.0 (Reflection AI) are two of the models people most often weigh against each other in 2026. GPT-5.6 Terra is the mid-tier daily driver of the GPT-5.6 family — near-flagship quality at about half of Sol's cost. 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 and open vs. closed weights — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecGPT-5.6 TerraReflection Beam 1.0
ProviderOpenAI (US) Reflection AI (US)
ReleasedJuly 9, 2026 October 16, 2026
Context window1M (~1,500 pages) 1M tokens (~1,500 pages)
Price (in/out)$2.5/$15 per 1M tokens Open weight (self-host / free)
Open weight?No — API only Yes — self-hostable
Modalitiestext, image, code text, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Balanced everyday work at roughly half of Sol's price

GPT-5.6 Terra

GPT-5.6 Terra lists balanced everyday work at roughly half of Sol's price among its strengths; Reflection Beam 1.0 does not.

Competitive with GPT-5.5 quality at about 2x lower cost

GPT-5.6 Terra

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

Solid agentic coding (Terminal-Bench 2.1 in the mid-80s)

GPT-5.6 Terra

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

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

Reflection Beam 1.0

GPT-5.6 Terra is comparatively weak here — no open weights

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 its weights are open while GPT-5.6 Terra is API-only.

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 is the newer of the two.

Lowest cost at scale

Reflection Beam 1.0

Its weights are open, so at volume you pay for your own hardware instead of GPT-5.6 Terra's $2.5/$15 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 GPT-5.6 Terra, and on millions of tokens that margin decides the monthly bill.

A team with data-privacy or self-hosting needs

→ Reflection Beam 1.0

Open weights let you run it on your own hardware; GPT-5.6 Terra is API-only.

Anyone whose priority is balanced everyday work at roughly half of sol's price

→ GPT-5.6 Terra

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-5.6 Terra: where it fits

The mid-tier daily driver of the GPT-5.6 family — near-flagship quality at about half of Sol's cost. Released July 9, 2026 by OpenAI, it is built for balanced everyday work at roughly half of Sol's price, competitive with GPT-5.5 quality at about 2x lower cost, solid agentic coding (Terminal-Bench 2.1 in the mid-80s), and same 1M context and programmatic tool calling as Sol.

Its trade-offs are real: fewer independently verified benchmarks than Sol, and trails it across coding evals, and no open weights. At $2.5 in / $15 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. GPT-5.6 Terra 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 GPT-5.6 Terra 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 GPT-5.6 Terra 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, GPT-5.6 Terra leans toward balanced everyday work at roughly half of sol's price 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-5.6 Terra 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 GPT-5.6 Terra is API-metered at $2.5/$15 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 (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.

Can I use both GPT-5.6 Terra and Reflection Beam 1.0 together?

Yes — a multi-model platform like LumiChats gives you GPT-5.6 Terra, 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-5.6 Terra or Reflection Beam 1.0?

Reflection Beam 1.0 — released October 16, 2026, about 3 months after GPT-5.6 Terra.

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.