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. Pick Step 3.7 Flash for a 198b-parameter sparse moe vision-language model (11b active) built specifically for agentic coding and search workflows or scmp reported it 'outshines larger rivals' from deepseek and moonshot on some benchmarks despite its smaller active size. On a tight budget at scale, Muse Glimmer is the value pick.
Muse Glimmer (Meta, US) and Step 3.7 Flash (StepFun, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. Step 3.7 Flash is stepFun's 198B MoE vision-language model built for coding and search agents - a distinct Chinese lab with genuinely competitive open weights. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Context window: Step 3.7 Flash holds 2× more — 256K (~393 pages) vs 128K (~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: Muse Glimmer is the newer model by about 2 months (released August 10, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
Muse Glimmer
Step 3.7 Flash
Provider
Meta (US)
StepFun (China)
Released
August 10, 2026
May 29, 2026
Context window
128K (~197 pages)
256K (~393 pages)
Price (in/out)
Open weight (self-host / free)
$0.2/$1.15 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
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; Step 3.7 Flash 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; Step 3.7 Flash does not.
A 198B-parameter sparse MoE vision-language model (11B active) built specifically for agentic coding and search workflows: Step 3.7 Flash — Muse Glimmer is comparatively weak here — a distilled ~30B model - ceiling below its closed teacher (Muse Spark) and cloud flagships
SCMP reported it 'outshines larger rivals' from DeepSeek and Moonshot on some benchmarks despite its smaller active size: Step 3.7 Flash — Muse Glimmer is comparatively weak here — published benchmarks (e.g. SWE-Bench Verified 76.0) are Meta's own
Open weights (Apache 2.0) at a low per-token price: Step 3.7 Flash — Its 256K window holds about 2× more than Muse Glimmer's 128K in a single prompt.
Lowest cost at scale: Muse Glimmer — Its weights are open, so at volume you pay for your own hardware instead of Step 3.7 Flash's $0.2/$1.15 per 1M tokens.
Largest single-prompt input: Step 3.7 Flash — Its 256K window is about 2× larger than Muse Glimmer's 128K, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Muse Glimmer — At Open weight (self-host / free) it undercuts Step 3.7 Flash, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Step 3.7 Flash — Larger 256K window fits more in one prompt.
Anyone whose priority is runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit: Muse Glimmer — It is specifically built for that.
Anyone whose priority is a 198b-parameter sparse moe vision-language model (11b active) built specifically for agentic coding and search workflows: Step 3.7 Flash — That is its strongest area.
An enterprise with regional data-residency rules: Muse Glimmer or Step 3.7 Flash — Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: 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.
Step 3.7 Flash: where it fits
StepFun's 198B MoE vision-language model built for coding and search agents - a distinct Chinese lab with genuinely competitive open weights. Released May 29, 2026 by StepFun, it is built for a 198B-parameter sparse MoE vision-language model (11B active) built specifically for agentic coding and search workflows, sCMP reported it 'outshines larger rivals' from DeepSeek and Moonshot on some benchmarks despite its smaller active size, open weights (Apache 2.0) at a low per-token price, and includes a 1.8B vision encoder for image understanding alongside text.
Its trade-offs: stepFun is a newer, less established lab than DeepSeek, Alibaba, or Moonshot, benchmark 'outshines rivals' claims are from press coverage of specific tests, not a full independent leaderboard sweep, and smaller ecosystem and less third-party documentation than the more established Chinese labs. At $0.2 in / $1.15 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." Muse Glimmer (US) and Step 3.7 Flash (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Muse Glimmer is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Frequently asked questions
Is Muse Glimmer or Step 3.7 Flash 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, Muse Glimmer leans toward runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit while Step 3.7 Flash leans toward a 198b-parameter sparse moe vision-language model (11b active) built specifically for agentic coding and search workflows, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Muse Glimmer or Step 3.7 Flash?
Muse Glimmer is cheaper — Open weight (self-host / free) vs $0.2/$1.15 per 1M tokens.
Which has the bigger context window?
Step 3.7 Flash — 256K vs 128K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Muse Glimmer and Step 3.7 Flash together?
Yes — a multi-model platform like LumiChats gives you Muse Glimmer, Step 3.7 Flash 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, Muse Glimmer or Step 3.7 Flash?
Muse Glimmer — released August 10, 2026, about 2 months after Step 3.7 Flash.
Muse Glimmer vs Step 3.7 Flash
Meta · US | StepFun · China · Updated June 2026
Quick verdict
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. Pick Step 3.7 Flash for a 198b-parameter sparse moe vision-language model (11b active) built specifically for agentic coding and search workflows or scmp reported it 'outshines larger rivals' from deepseek and moonshot on some benchmarks despite its smaller active size. On a tight budget at scale, Muse Glimmer is the value pick.
Muse Glimmer (Meta, US) and Step 3.7 Flash (StepFun, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. Step 3.7 Flash is stepFun's 198B MoE vision-language model built for coding and search agents - a distinct Chinese lab with genuinely competitive open weights. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Context window: Step 3.7 Flash holds 2× more — 256K (~393 pages) vs 128K (~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: Muse Glimmer is the newer model by about 2 months (released August 10, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
Muse Glimmer
Step 3.7 Flash
Provider
Meta (US)
StepFun (China)
Released
August 10, 2026
May 29, 2026
Context window
128K (~197 pages)
256K (~393 pages)
Price (in/out)
Open weight (self-host / free)
$0.2/$1.15 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
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; Step 3.7 Flash 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; Step 3.7 Flash does not.
A 198B-parameter sparse MoE vision-language model (11B active) built specifically for agentic coding and search workflows
Step 3.7 Flash
Muse Glimmer is comparatively weak here — a distilled ~30B model - ceiling below its closed teacher (Muse Spark) and cloud flagships
SCMP reported it 'outshines larger rivals' from DeepSeek and Moonshot on some benchmarks despite its smaller active size
Step 3.7 Flash
Muse Glimmer is comparatively weak here — published benchmarks (e.g. SWE-Bench Verified 76.0) are Meta's own
Open weights (Apache 2.0) at a low per-token price
Step 3.7 Flash
Its 256K window holds about 2× more than Muse Glimmer's 128K in a single prompt.
Lowest cost at scale
Muse Glimmer
Its weights are open, so at volume you pay for your own hardware instead of Step 3.7 Flash's $0.2/$1.15 per 1M tokens.
Largest single-prompt input
Step 3.7 Flash
Its 256K window is about 2× larger than Muse Glimmer's 128K, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Muse Glimmer
At Open weight (self-host / free) it undercuts Step 3.7 Flash, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Step 3.7 Flash
Larger 256K window fits more in one prompt.
Anyone whose priority is runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit
→ Muse Glimmer
It is specifically built for that.
Anyone whose priority is a 198b-parameter sparse moe vision-language model (11b active) built specifically for agentic coding and search workflows
→ Step 3.7 Flash
That is its strongest area.
An enterprise with regional data-residency rules
→ Muse Glimmer or Step 3.7 Flash
Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: 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.
Step 3.7 Flash: where it fits
StepFun's 198B MoE vision-language model built for coding and search agents - a distinct Chinese lab with genuinely competitive open weights. Released May 29, 2026 by StepFun, it is built for a 198B-parameter sparse MoE vision-language model (11B active) built specifically for agentic coding and search workflows, sCMP reported it 'outshines larger rivals' from DeepSeek and Moonshot on some benchmarks despite its smaller active size, open weights (Apache 2.0) at a low per-token price, and includes a 1.8B vision encoder for image understanding alongside text.
Its trade-offs: stepFun is a newer, less established lab than DeepSeek, Alibaba, or Moonshot, benchmark 'outshines rivals' claims are from press coverage of specific tests, not a full independent leaderboard sweep, and smaller ecosystem and less third-party documentation than the more established Chinese labs. At $0.2 in / $1.15 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." Muse Glimmer (US) and Step 3.7 Flash (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Muse Glimmer is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Want both Muse Glimmer and Step 3.7 Flash 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 Muse Glimmer or Step 3.7 Flash 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, Muse Glimmer leans toward runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit while Step 3.7 Flash leans toward a 198b-parameter sparse moe vision-language model (11b active) built specifically for agentic coding and search workflows, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Muse Glimmer or Step 3.7 Flash?
Muse Glimmer is cheaper — Open weight (self-host / free) vs $0.2/$1.15 per 1M tokens.
Which has the bigger context window?
Step 3.7 Flash — 256K vs 128K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Muse Glimmer and Step 3.7 Flash together?
Yes — a multi-model platform like LumiChats gives you Muse Glimmer, Step 3.7 Flash 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, Muse Glimmer or Step 3.7 Flash?
Muse Glimmer — released August 10, 2026, about 2 months after Step 3.7 Flash.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.