Pick Mercury 2.5 Preview for very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation or coding accuracy (95.7%, 91st percentile among cost-optimized models). 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. Choose Muse Glimmer if you need self-hosting or data privacy; Mercury 2.5 Preview if you want a managed API.
Mercury 2.5 Preview (Inception Labs) and Muse Glimmer (Meta) are two of the models people most often weigh against each other in 2026. Mercury 2.5 Preview is inception Labs' August 31, 2026 diffusion-based language model preview, refining tokens in parallel for roughly 10x the throughput of comparable autoregressive models at cost-optimized-tier quality. 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. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Cost model: Muse Glimmer ships open weights you can self-host (hardware cost only, no per-token fee), while Mercury 2.5 Preview is API-metered at $0.04/$0.15 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
Context window: Mercury 2.5 Preview holds 2× more — 260K tokens (~390 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: Mercury 2.5 Preview is the newer model by about 21 days (released August 31, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Mercury 2.5 Preview
Muse Glimmer
Provider
Inception Labs (US)
Meta (US)
Released
August 31, 2026
August 10, 2026
Context window
260K tokens (~390 pages)
128K (~197 pages)
Price (in/out)
$0.04/$0.15 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
Yes — self-hostable
Modalities
text
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation: Mercury 2.5 Preview — Its 260K tokens window holds about 2× more than Muse Glimmer's 128K in a single prompt.
Coding accuracy (95.7%, 91st percentile among cost-optimized models): Mercury 2.5 Preview — Muse Glimmer is comparatively weak here — 4-bit quantization to fit consumer GPUs trades away some accuracy
Mathematics accuracy (97.0%, 97th percentile): Mercury 2.5 Preview — Inception Labs' August 31, 2026 diffusion-based language model preview, refining tokens in parallel for roughly 10x the throughput of comparable autoregressive models at cost-optimized-tier quality — and it carries the larger 260K tokens context.
Runs real agentic tasks locally on one consumer GPU - fits 24GB of VRAM in 4-bit: Muse Glimmer — Open weights make this possible at all — Mercury 2.5 Preview is API-only, so it cannot leave the vendor's servers.
Open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine: 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 its weights are open while Mercury 2.5 Preview is API-only.
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; Mercury 2.5 Preview does not.
Lowest cost at scale: Muse Glimmer — Its weights are open, so at volume you pay for your own hardware instead of Mercury 2.5 Preview's $0.04/$0.15 per 1M tokens.
Largest single-prompt input: Mercury 2.5 Preview — Its 260K tokens window is about 2× larger than Muse Glimmer's 128K, fitting roughly 390 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 Mercury 2.5 Preview, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Mercury 2.5 Preview — Larger 260K tokens window fits more in one prompt.
A team with data-privacy or self-hosting needs: Muse Glimmer — Open weights let you run it on your own hardware; Mercury 2.5 Preview is API-only.
Anyone whose priority is very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation: Mercury 2.5 Preview — 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.
Mercury 2.5 Preview: where it fits
Inception Labs' August 31, 2026 diffusion-based language model preview, refining tokens in parallel for roughly 10x the throughput of comparable autoregressive models at cost-optimized-tier quality. Released August 31, 2026 by Inception Labs, it is built for very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation, coding accuracy (95.7%, 91st percentile among cost-optimized models), mathematics accuracy (97.0%, 97th percentile), and tunable reasoning levels with parallel tool calls and schema-aligned JSON output.
Its trade-offs are real: 260K context window is far shorter than frontier 1M-token models, no vision or audio modalities, positioned only against cost-optimized models (GPT-5.6 Luna Low, Gemini 3.5 Flash-Lite), not frontier-class, and current $0.04/$0.15 pricing includes a limited-time launch promotion on OpenRouter (list price is $0.20/$0.75, may rise after Sept 8, 2026). At $0.04 in / $0.15 out per million tokens, it sits in the budget price band.
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
The defining split here is open vs. closed. Muse Glimmer gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Mercury 2.5 Preview 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.
Frequently asked questions
Is Mercury 2.5 Preview or Muse Glimmer 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, Mercury 2.5 Preview leans toward very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation 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, Mercury 2.5 Preview or Muse Glimmer?
Muse Glimmer is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Mercury 2.5 Preview is API-metered at $0.04/$0.15 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?
Mercury 2.5 Preview — 260K tokens vs 128K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Mercury 2.5 Preview and Muse Glimmer together?
Yes — a multi-model platform like LumiChats gives you Mercury 2.5 Preview, 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, Mercury 2.5 Preview or Muse Glimmer?
Mercury 2.5 Preview — released August 31, 2026, about 21 days after Muse Glimmer.
Mercury 2.5 Preview vs Muse Glimmer
Inception Labs · US | Meta · US · Updated June 2026
Quick verdict
Pick Mercury 2.5 Preview for very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation or coding accuracy (95.7%, 91st percentile among cost-optimized models). 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. Choose Muse Glimmer if you need self-hosting or data privacy; Mercury 2.5 Preview if you want a managed API.
Mercury 2.5 Preview (Inception Labs) and Muse Glimmer (Meta) are two of the models people most often weigh against each other in 2026. Mercury 2.5 Preview is inception Labs' August 31, 2026 diffusion-based language model preview, refining tokens in parallel for roughly 10x the throughput of comparable autoregressive models at cost-optimized-tier quality. 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. 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
▸Cost model: Muse Glimmer ships open weights you can self-host (hardware cost only, no per-token fee), while Mercury 2.5 Preview is API-metered at $0.04/$0.15 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
▸Context window: Mercury 2.5 Preview holds 2× more — 260K tokens (~390 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: Mercury 2.5 Preview is the newer model by about 21 days (released August 31, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Mercury 2.5 Preview
Muse Glimmer
Provider
Inception Labs (US)
Meta (US)
Released
August 31, 2026
August 10, 2026
Context window
260K tokens (~390 pages)
128K (~197 pages)
Price (in/out)
$0.04/$0.15 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
Yes — self-hostable
Modalities
text
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation
Mercury 2.5 Preview
Its 260K tokens window holds about 2× more than Muse Glimmer's 128K in a single prompt.
Coding accuracy (95.7%, 91st percentile among cost-optimized models)
Mercury 2.5 Preview
Muse Glimmer is comparatively weak here — 4-bit quantization to fit consumer GPUs trades away some accuracy
Mathematics accuracy (97.0%, 97th percentile)
Mercury 2.5 Preview
Inception Labs' August 31, 2026 diffusion-based language model preview, refining tokens in parallel for roughly 10x the throughput of comparable autoregressive models at cost-optimized-tier quality — and it carries the larger 260K tokens context.
Runs real agentic tasks locally on one consumer GPU - fits 24GB of VRAM in 4-bit
Muse Glimmer
Open weights make this possible at all — Mercury 2.5 Preview is API-only, so it cannot leave the vendor's servers.
Open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine
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 its weights are open while Mercury 2.5 Preview is API-only.
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; Mercury 2.5 Preview does not.
Lowest cost at scale
Muse Glimmer
Its weights are open, so at volume you pay for your own hardware instead of Mercury 2.5 Preview's $0.04/$0.15 per 1M tokens.
Largest single-prompt input
Mercury 2.5 Preview
Its 260K tokens window is about 2× larger than Muse Glimmer's 128K, fitting roughly 390 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 Mercury 2.5 Preview, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Mercury 2.5 Preview
Larger 260K tokens window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ Muse Glimmer
Open weights let you run it on your own hardware; Mercury 2.5 Preview is API-only.
Anyone whose priority is very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation
→ Mercury 2.5 Preview
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.
Mercury 2.5 Preview: where it fits
Inception Labs' August 31, 2026 diffusion-based language model preview, refining tokens in parallel for roughly 10x the throughput of comparable autoregressive models at cost-optimized-tier quality. Released August 31, 2026 by Inception Labs, it is built for very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation, coding accuracy (95.7%, 91st percentile among cost-optimized models), mathematics accuracy (97.0%, 97th percentile), and tunable reasoning levels with parallel tool calls and schema-aligned JSON output.
Its trade-offs are real: 260K context window is far shorter than frontier 1M-token models, no vision or audio modalities, positioned only against cost-optimized models (GPT-5.6 Luna Low, Gemini 3.5 Flash-Lite), not frontier-class, and current $0.04/$0.15 pricing includes a limited-time launch promotion on OpenRouter (list price is $0.20/$0.75, may rise after Sept 8, 2026). At $0.04 in / $0.15 out per million tokens, it sits in the budget price band.
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
The defining split here is open vs. closed. Muse Glimmer gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Mercury 2.5 Preview 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 Mercury 2.5 Preview 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 Mercury 2.5 Preview or Muse Glimmer 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, Mercury 2.5 Preview leans toward very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation 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, Mercury 2.5 Preview or Muse Glimmer?
Muse Glimmer is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Mercury 2.5 Preview is API-metered at $0.04/$0.15 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?
Mercury 2.5 Preview — 260K tokens vs 128K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Mercury 2.5 Preview and Muse Glimmer together?
Yes — a multi-model platform like LumiChats gives you Mercury 2.5 Preview, 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, Mercury 2.5 Preview or Muse Glimmer?
Mercury 2.5 Preview — released August 31, 2026, about 21 days after Muse Glimmer.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.