GPT-4o mini vs Muse Spark Open

OpenAI · US  |  Meta · US · Updated June 2026

Quick verdict

Pick GPT-4o mini for very low cost per token for its capability tier or strong coding for a small model (87.2% humaneval). Pick Muse Spark Open for meta's first open-weight muse model - 816b-parameter moe with 42b active, accepting text, image, video and audio input or artificial analysis intelligence index of 39, first among us open-weight models. Choose Muse Spark Open if you need self-hosting or data privacy; GPT-4o mini if you want a managed API.

GPT-4o mini (OpenAI) and Muse Spark Open (Meta) are two of the models people most often weigh against each other in 2026. GPT-4o mini is openAI's budget small multimodal model — cheap, fast text-and-vision intelligence that outscored peer small models like Gemini 1.5 Flash and Claude 3 Haiku on MMLU and HumanEval at launch. Muse Spark Open is meta's October 15, 2026 open-weight Muse Spark - an 816B/42B-active multimodal MoE with a 1M context under a gated Muse Model License. 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-4o miniMuse Spark Open
ProviderOpenAI (US) Meta (US)
ReleasedJuly 18, 2024 October 15, 2026
Context window128K (~192 pages) 1M tokens (~1,500 pages)
Price (in/out)$0.15/$0.6 per 1M tokens Open weight (self-host / free)
Open weight?No — API only Yes — self-hostable
Modalitiestext, image text, image, video, audio, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Very low cost per token for its capability tier

GPT-4o mini

Muse Spark Open is comparatively weak here — needs about 1 TB of memory at 4-bit; no first-party per-token price confirmed

Strong coding for a small model (87.2% HumanEval)

GPT-4o mini

Muse Spark Open is comparatively weak here — weakest of the three Muse models (Wonder Full scores 56, Wonder 52)

Leading MMLU among peer small models (82%)

GPT-4o mini

GPT-4o mini lists leading MMLU among peer small models (82%) among its strengths; Muse Spark Open does not.

Meta's first open-weight Muse model - 816B-parameter MoE with 42B active, accepting text, image, video and audio input

Muse Spark Open

Open weights make this possible at all — GPT-4o mini is API-only, so it cannot leave the vendor's servers.

Artificial Analysis Intelligence Index of 39, first among US open-weight models

Muse Spark Open

GPT-4o mini is comparatively weak here — weaker on hard reasoning and coding than frontier models

About 143 output tokens/sec; up to 128K output tokens

Muse Spark Open

Its 1M tokens window holds about 7.8× more than GPT-4o mini's 128K in a single prompt.

Lowest cost at scale

Muse Spark Open

Its weights are open, so at volume you pay for your own hardware instead of GPT-4o mini's $0.15/$0.6 per 1M tokens.

Largest single-prompt input

Muse Spark Open

Its 1M tokens window is about 7.8× larger than GPT-4o mini's 128K, fitting roughly 1,500 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

→ Muse Spark Open

At Open weight (self-host / free) it undercuts GPT-4o mini, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

→ Muse Spark Open

Larger 1M tokens window fits more in one prompt.

A team with data-privacy or self-hosting needs

→ Muse Spark Open

Open weights let you run it on your own hardware; GPT-4o mini is API-only.

Anyone whose priority is very low cost per token for its capability tier

→ GPT-4o mini

It is specifically built for that.

Anyone whose priority is meta's first open-weight muse model - 816b-parameter moe with 42b active, accepting text, image, video and audio input

→ Muse Spark Open

That is its strongest area.

GPT-4o mini: where it fits

OpenAI's budget small multimodal model — cheap, fast text-and-vision intelligence that outscored peer small models like Gemini 1.5 Flash and Claude 3 Haiku on MMLU and HumanEval at launch. Released July 18, 2024 by OpenAI, it is built for very low cost per token for its capability tier, strong coding for a small model (87.2% HumanEval), leading MMLU among peer small models (82%), and text and image (vision) understanding in the API.

Its trade-offs are real: only 128K context with an October 2023 knowledge cutoff, and weaker on hard reasoning and coding than frontier models. At $0.15 in / $0.6 out per million tokens, it sits in the budget price band.

Muse Spark Open: where it fits

Meta's October 15, 2026 open-weight Muse Spark - an 816B/42B-active multimodal MoE with a 1M context under a gated Muse Model License. Released October 15, 2026 by Meta, it is built for meta's first open-weight Muse model - 816B-parameter MoE with 42B active, accepting text, image, video and audio input, artificial Analysis Intelligence Index of 39, first among US open-weight models, and about 143 output tokens/sec; up to 128K output tokens.

Its trade-offs: weakest of the three Muse models (Wonder Full scores 56, Wonder 52), muse Model License: attribution required and a revenue threshold (reported as about $20M; check the license text) before a separate license is needed, and needs about 1 TB of memory at 4-bit; no first-party per-token price confirmed. 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 Spark Open gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. GPT-4o mini 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-4o mini and Muse Spark Open 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-4o mini or Muse Spark Open 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-4o mini leans toward very low cost per token for its capability tier while Muse Spark Open leans toward meta's first open-weight muse model - 816b-parameter moe with 42b active, accepting text, image, video and audio input, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, GPT-4o mini or Muse Spark Open?

Muse Spark Open is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-4o mini is API-metered at $0.15/$0.6 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 Open — 1M tokens vs 128K, about 7.8× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both GPT-4o mini and Muse Spark Open together?

Yes — a multi-model platform like LumiChats gives you GPT-4o mini, Muse Spark Open 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-4o mini or Muse Spark Open?

Muse Spark Open — released October 15, 2026, about 27 months after GPT-4o mini.

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.