Pick gpt-oss-120b for self-hostable on a single 80gb h100 gpu via mxfp4 or configurable reasoning depth (low/medium/high). Pick Muse Glimmer for runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit or open weights (apache 2.0), free to self-host and offline-capable - data never leaves your machine.
gpt-oss-120b (OpenAI) and Muse Glimmer (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 Glimmer is meta's open ~30B agentic model - runs tool-using AI agents locally on a single consumer GPU, offline and free, though it trails frontier cloud models. The breakdown below works through their capabilities and ideal use cases so you can match one to your task.
Key differences
Context window: both advertise 131K (~197 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
Recency: Muse Glimmer is the newer model by about 12 months (released August 10, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
gpt-oss-120b
Muse Glimmer
Provider
OpenAI (US)
Meta (US)
Released
August 5, 2025
August 10, 2026
Context window
131K (~197 pages)
128K (~197 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, image, 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; Muse Glimmer does not.
Configurable reasoning depth (low/medium/high): gpt-oss-120b — gpt-oss-120b lists configurable reasoning depth (low/medium/high) among its strengths; Muse Glimmer does not.
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; Muse Glimmer does not.
Runs real agentic tasks locally on one consumer GPU - fits 24GB of VRAM in 4-bit: Muse Glimmer — Meta's open ~30B agentic model - runs tool-using AI agents locally on a single consumer GPU, offline and free, though it trails frontier cloud models — and it is the newer of the two.
Open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine: Muse Glimmer — Muse Glimmer lists open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine among its strengths; gpt-oss-120b does not.
Agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery: Muse Glimmer — Muse Glimmer lists agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery among its strengths; gpt-oss-120b does not.
Which should you pick?
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 runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit: Muse Glimmer — 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 Glimmer: where it fits
Meta's open ~30B agentic model - runs tool-using AI agents locally on a single consumer GPU, offline and free, though it trails frontier cloud models. Released August 10, 2026 by Meta, it is built for runs real agentic tasks locally on one consumer GPU - fits 24GB of VRAM in 4-bit, open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine, agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery, and 128K context, text and image input, trained on 100+ languages.
Its trade-offs: a distilled ~30B model - ceiling below its closed teacher (Muse Spark) and cloud flagships, no independent Artificial Analysis intelligence score published yet, published benchmarks (e.g. SWE-Bench Verified 76.0) are Meta's own, and 4-bit quantization to fit consumer GPUs trades away some accuracy. 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 Muse Glimmer overlap enough that the right pick depends on your specific job. and each leads in its own area — gpt-oss-120b for self-hostable on a single 80gb h100 gpu via mxfp4, Muse Glimmer for runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit. Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is gpt-oss-120b or Muse Glimmer better for coding?
Public SWE-Bench figures are not available for Muse Glimmer, 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 Glimmer leans toward runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, gpt-oss-120b or Muse Glimmer?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
Both advertise 131K (~197 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both gpt-oss-120b and Muse Glimmer together?
Yes — a multi-model platform like LumiChats gives you gpt-oss-120b, Muse Glimmer 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 Glimmer?
Muse Glimmer — released August 10, 2026, about 12 months after gpt-oss-120b.
gpt-oss-120b vs Muse Glimmer
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 Glimmer for runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit or open weights (apache 2.0), free to self-host and offline-capable - data never leaves your machine.
gpt-oss-120b (OpenAI) and Muse Glimmer (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 Glimmer is meta's open ~30B agentic model - runs tool-using AI agents locally on a single consumer GPU, offline and free, though it trails frontier cloud models. The breakdown below works through their capabilities and ideal use cases so you can match one to your task.
Key differences at a glance
▸Context window: both advertise 131K (~197 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
▸Recency: Muse Glimmer is the newer model by about 12 months (released August 10, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
gpt-oss-120b
Muse Glimmer
Provider
OpenAI (US)
Meta (US)
Released
August 5, 2025
August 10, 2026
Context window
131K (~197 pages)
128K (~197 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, image, 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; Muse Glimmer does not.
Configurable reasoning depth (low/medium/high)
gpt-oss-120b
gpt-oss-120b lists configurable reasoning depth (low/medium/high) among its strengths; Muse Glimmer does not.
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; Muse Glimmer does not.
Runs real agentic tasks locally on one consumer GPU - fits 24GB of VRAM in 4-bit
Muse Glimmer
Meta's open ~30B agentic model - runs tool-using AI agents locally on a single consumer GPU, offline and free, though it trails frontier cloud models — and it is the newer of the two.
Open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine
Muse Glimmer
Muse Glimmer lists open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine among its strengths; gpt-oss-120b does not.
Agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery
Muse Glimmer
Muse Glimmer lists agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery among its strengths; gpt-oss-120b does not.
Which should you pick?
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 runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit
→ Muse Glimmer
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 Glimmer: where it fits
Meta's open ~30B agentic model - runs tool-using AI agents locally on a single consumer GPU, offline and free, though it trails frontier cloud models. Released August 10, 2026 by Meta, it is built for runs real agentic tasks locally on one consumer GPU - fits 24GB of VRAM in 4-bit, open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine, agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery, and 128K context, text and image input, trained on 100+ languages.
Its trade-offs: a distilled ~30B model - ceiling below its closed teacher (Muse Spark) and cloud flagships, no independent Artificial Analysis intelligence score published yet, published benchmarks (e.g. SWE-Bench Verified 76.0) are Meta's own, and 4-bit quantization to fit consumer GPUs trades away some accuracy. 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 Muse Glimmer overlap enough that the right pick depends on your specific job. and each leads in its own area — gpt-oss-120b for self-hostable on a single 80gb h100 gpu via mxfp4, Muse Glimmer for runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both gpt-oss-120b and Muse Glimmer 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.
Is gpt-oss-120b or Muse Glimmer better for coding?
Public SWE-Bench figures are not available for Muse Glimmer, 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 Glimmer leans toward runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, gpt-oss-120b or Muse Glimmer?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
Both advertise 131K (~197 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both gpt-oss-120b and Muse Glimmer together?
Yes — a multi-model platform like LumiChats gives you gpt-oss-120b, Muse Glimmer 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 Glimmer?
Muse Glimmer — released August 10, 2026, about 12 months after gpt-oss-120b.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.