gpt-oss-120b vs Muse Spark 1.3

OpenAI · US  |  Meta · 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 Muse Spark 1.3 for agentic coding with ~20% fewer tool calls and ~25% fewer tokens than muse spark 1.2 or deepswe v1.1 long-horizon software engineering (75.4). Choose gpt-oss-120b if you need self-hosting or data privacy; Muse Spark 1.3 if you want a managed API.

gpt-oss-120b (OpenAI) and Muse Spark 1.3 (Meta) 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. Muse Spark 1.3 is meta's September 2, 2026 agentic-coding model update, cutting tool calls and token usage versus Muse Spark 1.2 while its highest-reasoning mode remains gated for safety review. 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

Specgpt-oss-120bMuse Spark 1.3
ProviderOpenAI (US) Meta (US)
ReleasedAugust 5, 2025 September 2, 2026
Context window131K (~197 pages) 1M tokens (~1,500 pages)
Price (in/out)Open weight (self-host / free) $1.25/$4.25 per 1M tokens
Open weight?Yes — self-hostable No — API only
Modalitiestext, code text, image, video
SWE-Bench Verified62.4% Not published
MRCR v2 @ 1MNot published 98.1%

Who wins what

Self-hostable on a single 80GB H100 GPU via MXFP4

gpt-oss-120b

Open weights make this possible at all — Muse Spark 1.3 is API-only, so it cannot leave the vendor's servers.

Configurable reasoning depth (low/medium/high)

gpt-oss-120b

Muse Spark 1.3 is comparatively weak here — strongest 'max' reasoning configuration still gated pending additional safety testing

Agentic tool use, function calling, and code execution

gpt-oss-120b

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 — and its weights are open while Muse Spark 1.3 is API-only.

Agentic coding with ~20% fewer tool calls and ~25% fewer tokens than Muse Spark 1.2

Muse Spark 1.3

Its 1M tokens window holds about 7.6× more than gpt-oss-120b's 131K in a single prompt.

DeepSWE v1.1 long-horizon software engineering (75.4)

Muse Spark 1.3

Meta's September 2, 2026 agentic-coding model update, cutting tool calls and token usage versus Muse Spark 1.2 while its highest-reasoning mode remains gated for safety review — and it carries the larger 1M tokens context.

Near-perfect long-context retrieval (MRCR v2 512K-1M: 98.1)

Muse Spark 1.3

gpt-oss-120b is comparatively weak here — 131K context and 5.1B active params trail the largest frontier closed models

Lowest cost at scale

gpt-oss-120b

Its weights are open, so at volume you pay for your own hardware instead of Muse Spark 1.3's $1.25/$4.25 per 1M tokens.

Largest single-prompt input

Muse Spark 1.3

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?

A cost-sensitive startup shipping high volume

→ gpt-oss-120b

At Open weight (self-host / free) it undercuts Muse Spark 1.3, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

→ Muse Spark 1.3

Larger 1M tokens window fits more in one prompt.

A team with data-privacy or self-hosting needs

→ gpt-oss-120b

Open weights let you run it on your own hardware; Muse Spark 1.3 is API-only.

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 agentic coding with ~20% fewer tool calls and ~25% fewer tokens than muse spark 1.2

→ Muse Spark 1.3

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.

Muse Spark 1.3: where it fits

Meta's September 2, 2026 agentic-coding model update, cutting tool calls and token usage versus Muse Spark 1.2 while its highest-reasoning mode remains gated for safety review. Released September 2, 2026 by Meta, it is built for agentic coding with ~20% fewer tool calls and ~25% fewer tokens than Muse Spark 1.2, deepSWE v1.1 long-horizon software engineering (75.4), near-perfect long-context retrieval (MRCR v2 512K-1M: 98.1), and ranks third overall on the Artificial Analysis Intelligence Index (score 61, xhigh variant; the limited-preview max variant scores 62) - behind only Claude Fable 5.1 and Claude Opus 5.

Its trade-offs: strongest 'max' reasoning configuration still gated pending additional safety testing, not yet open-weight, despite Meta roadmapping a future Muse Spark weights release, and no official SWE-bench Verified score published. At $1.25 in / $4.25 out per million tokens, it sits in the mid price band.

The bottom line for this matchup

The defining split here is open vs. closed. gpt-oss-120b gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Muse Spark 1.3 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-oss-120b and Muse Spark 1.3 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-oss-120b or Muse Spark 1.3 better for coding?

Public SWE-Bench figures are not available for Muse Spark 1.3, 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 Muse Spark 1.3 leans toward agentic coding with ~20% fewer tool calls and ~25% fewer tokens than muse spark 1.2, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, gpt-oss-120b or Muse Spark 1.3?

gpt-oss-120b is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Muse Spark 1.3 is API-metered at $1.25/$4.25 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?

Muse Spark 1.3 — 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 Muse Spark 1.3 together?

Yes — a multi-model platform like LumiChats gives you gpt-oss-120b, Muse Spark 1.3 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 Muse Spark 1.3?

Muse Spark 1.3 — released September 2, 2026, about 13 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.