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 1.1 for scaled tool use — 88.1 on mcp atlas, ahead of opus 4.8 and gpt-5.5 (vendor-reported) or subagent orchestration — trained to run as a main agent or a subagent that escalates when stuck. On a tight budget at scale, GPT-4o mini is the value pick.
GPT-4o mini (OpenAI) and Muse Spark 1.1 (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 1.1 is meta's first paid, closed-weight frontier model — class-leading agentic tool use at a quarter of rivals' price, but it trails on coding. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: GPT-4o mini is about 8.3× cheaper on input ($0.15/$0.6 per 1M tokens vs $1.25/$4.25 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
Context window: Muse Spark 1.1 holds 8.2× more — 1M (~1,573 pages) vs 128K (~192 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 Spark 1.1 is the newer model by about 24 months (released July 9, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GPT-4o mini
Muse Spark 1.1
Provider
OpenAI (US)
Meta (US)
Released
July 18, 2024
July 9, 2026
Context window
128K (~192 pages)
1M (~1,573 pages)
Price (in/out)
$0.15/$0.6 per 1M tokens
$1.25/$4.25 per 1M tokens
Open weight?
No — API only
No — API only
Modalities
text, image
text, image, video, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
54.1%
Who wins what
Very low cost per token for its capability tier: GPT-4o mini — At $0.15/$0.6 per 1M tokens it undercuts Muse Spark 1.1 ($1.25/$4.25 per 1M tokens), and that gap compounds at volume.
Strong coding for a small model (87.2% HumanEval): GPT-4o mini — Muse Spark 1.1 is comparatively weak here — not the coding leader its launch framing implied — Meta's own report concedes it trails Opus 4.8 and GPT-5.5 on every coding benchmark
Leading MMLU among peer small models (82%): GPT-4o mini — 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 — and it runs cheaper at $0.15/$0.6 per 1M tokens.
Scaled tool use — 88.1 on MCP Atlas, ahead of Opus 4.8 and GPT-5.5 (vendor-reported): Muse Spark 1.1 — Meta's first paid, closed-weight frontier model — class-leading agentic tool use at a quarter of rivals' price, but it trails on coding — and it carries the larger 1M context.
Subagent orchestration — trained to run as a main agent or a subagent that escalates when stuck: Muse Spark 1.1 — Meta's first paid, closed-weight frontier model — class-leading agentic tool use at a quarter of rivals' price, but it trails on coding — and it is the newer of the two.
Professional agentic work — 54.7 on JobBench, a wide margin over rivals (vendor-reported): Muse Spark 1.1 — Muse Spark 1.1 lists professional agentic work — 54.7 on JobBench, a wide margin over rivals (vendor-reported) among its strengths; GPT-4o mini does not.
Lowest cost at scale: GPT-4o mini — At $0.15/$0.6 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Muse Spark 1.1 — Its 1M window is about 8.2× larger than GPT-4o mini's 128K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: GPT-4o mini — At $0.15/$0.6 per 1M tokens it undercuts Muse Spark 1.1, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Muse Spark 1.1 — Larger 1M window fits more in one prompt.
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 scaled tool use — 88.1 on mcp atlas, ahead of opus 4.8 and gpt-5.5 (vendor-reported): Muse Spark 1.1 — 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 1.1: where it fits
Meta's first paid, closed-weight frontier model — class-leading agentic tool use at a quarter of rivals' price, but it trails on coding. Released July 9, 2026 by Meta, it is built for scaled tool use — 88.1 on MCP Atlas, ahead of Opus 4.8 and GPT-5.5 (vendor-reported), subagent orchestration — trained to run as a main agent or a subagent that escalates when stuck, professional agentic work — 54.7 on JobBench, a wide margin over rivals (vendor-reported), and managing its own context: it compacts the 1M window mid-run instead of relying on external windowing.
Its trade-offs: not the coding leader its launch framing implied — Meta's own report concedes it trails Opus 4.8 and GPT-5.5 on every coding benchmark, the 1M window oversells its recall: 54.1 on MRCR v2 at 1M against GPT-5.5's 74.0, closed weights end the free, self-hostable Llama path — this is the first model Meta has charged for, and uS-only public preview behind a waitlist, and every benchmark is vendor-reported with no third-party replication. At $1.25 in / $4.25 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
GPT-4o mini and Muse Spark 1.1 overlap enough that the right pick depends on your specific job. GPT-4o mini costs less per token; Muse Spark 1.1 holds the larger context; and each leads in its own area — GPT-4o mini for very low cost per token for its capability tier, Muse Spark 1.1 for scaled tool use — 88.1 on mcp atlas, ahead of opus 4.8 and gpt-5.5 (vendor-reported). Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is GPT-4o mini or Muse Spark 1.1 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 1.1 leans toward scaled tool use — 88.1 on mcp atlas, ahead of opus 4.8 and gpt-5.5 (vendor-reported), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GPT-4o mini or Muse Spark 1.1?
GPT-4o mini is cheaper — $0.15/$0.6 per 1M tokens vs $1.25/$4.25 per 1M tokens, roughly 8.3× apart on input.
Which has the bigger context window?
Muse Spark 1.1 — 1M vs 128K, about 8.2× 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 1.1 together?
Yes — a multi-model platform like LumiChats gives you GPT-4o mini, Muse Spark 1.1 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 1.1?
Muse Spark 1.1 — released July 9, 2026, about 24 months after GPT-4o mini.
GPT-4o mini vs Muse Spark 1.1
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 1.1 for scaled tool use — 88.1 on mcp atlas, ahead of opus 4.8 and gpt-5.5 (vendor-reported) or subagent orchestration — trained to run as a main agent or a subagent that escalates when stuck. On a tight budget at scale, GPT-4o mini is the value pick.
GPT-4o mini (OpenAI) and Muse Spark 1.1 (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 1.1 is meta's first paid, closed-weight frontier model — class-leading agentic tool use at a quarter of rivals' price, but it trails on coding. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: GPT-4o mini is about 8.3× cheaper on input ($0.15/$0.6 per 1M tokens vs $1.25/$4.25 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
▸Context window: Muse Spark 1.1 holds 8.2× more — 1M (~1,573 pages) vs 128K (~192 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 Spark 1.1 is the newer model by about 24 months (released July 9, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GPT-4o mini
Muse Spark 1.1
Provider
OpenAI (US)
Meta (US)
Released
July 18, 2024
July 9, 2026
Context window
128K (~192 pages)
1M (~1,573 pages)
Price (in/out)
$0.15/$0.6 per 1M tokens
$1.25/$4.25 per 1M tokens
Open weight?
No — API only
No — API only
Modalities
text, image
text, image, video, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
54.1%
Who wins what
Very low cost per token for its capability tier
GPT-4o mini
At $0.15/$0.6 per 1M tokens it undercuts Muse Spark 1.1 ($1.25/$4.25 per 1M tokens), and that gap compounds at volume.
Strong coding for a small model (87.2% HumanEval)
GPT-4o mini
Muse Spark 1.1 is comparatively weak here — not the coding leader its launch framing implied — Meta's own report concedes it trails Opus 4.8 and GPT-5.5 on every coding benchmark
Leading MMLU among peer small models (82%)
GPT-4o mini
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 — and it runs cheaper at $0.15/$0.6 per 1M tokens.
Scaled tool use — 88.1 on MCP Atlas, ahead of Opus 4.8 and GPT-5.5 (vendor-reported)
Muse Spark 1.1
Meta's first paid, closed-weight frontier model — class-leading agentic tool use at a quarter of rivals' price, but it trails on coding — and it carries the larger 1M context.
Subagent orchestration — trained to run as a main agent or a subagent that escalates when stuck
Muse Spark 1.1
Meta's first paid, closed-weight frontier model — class-leading agentic tool use at a quarter of rivals' price, but it trails on coding — and it is the newer of the two.
Professional agentic work — 54.7 on JobBench, a wide margin over rivals (vendor-reported)
Muse Spark 1.1
Muse Spark 1.1 lists professional agentic work — 54.7 on JobBench, a wide margin over rivals (vendor-reported) among its strengths; GPT-4o mini does not.
Lowest cost at scale
GPT-4o mini
At $0.15/$0.6 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Muse Spark 1.1
Its 1M window is about 8.2× larger than GPT-4o mini's 128K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ GPT-4o mini
At $0.15/$0.6 per 1M tokens it undercuts Muse Spark 1.1, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Muse Spark 1.1
Larger 1M window fits more in one prompt.
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 scaled tool use — 88.1 on mcp atlas, ahead of opus 4.8 and gpt-5.5 (vendor-reported)
→ Muse Spark 1.1
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 1.1: where it fits
Meta's first paid, closed-weight frontier model — class-leading agentic tool use at a quarter of rivals' price, but it trails on coding. Released July 9, 2026 by Meta, it is built for scaled tool use — 88.1 on MCP Atlas, ahead of Opus 4.8 and GPT-5.5 (vendor-reported), subagent orchestration — trained to run as a main agent or a subagent that escalates when stuck, professional agentic work — 54.7 on JobBench, a wide margin over rivals (vendor-reported), and managing its own context: it compacts the 1M window mid-run instead of relying on external windowing.
Its trade-offs: not the coding leader its launch framing implied — Meta's own report concedes it trails Opus 4.8 and GPT-5.5 on every coding benchmark, the 1M window oversells its recall: 54.1 on MRCR v2 at 1M against GPT-5.5's 74.0, closed weights end the free, self-hostable Llama path — this is the first model Meta has charged for, and uS-only public preview behind a waitlist, and every benchmark is vendor-reported with no third-party replication. At $1.25 in / $4.25 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
GPT-4o mini and Muse Spark 1.1 overlap enough that the right pick depends on your specific job. GPT-4o mini costs less per token; Muse Spark 1.1 holds the larger context; and each leads in its own area — GPT-4o mini for very low cost per token for its capability tier, Muse Spark 1.1 for scaled tool use — 88.1 on mcp atlas, ahead of opus 4.8 and gpt-5.5 (vendor-reported). Rather than crowning one, run the same hard task through both once and let the results decide.
Want both GPT-4o mini and Muse Spark 1.1 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-4o mini or Muse Spark 1.1 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 1.1 leans toward scaled tool use — 88.1 on mcp atlas, ahead of opus 4.8 and gpt-5.5 (vendor-reported), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GPT-4o mini or Muse Spark 1.1?
GPT-4o mini is cheaper — $0.15/$0.6 per 1M tokens vs $1.25/$4.25 per 1M tokens, roughly 8.3× apart on input.
Which has the bigger context window?
Muse Spark 1.1 — 1M vs 128K, about 8.2× 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 1.1 together?
Yes — a multi-model platform like LumiChats gives you GPT-4o mini, Muse Spark 1.1 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 1.1?
Muse Spark 1.1 — released July 9, 2026, about 24 months after GPT-4o mini.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.