Pick Kimi K2.5 for native multimodal reasoning and visual coding or agentic tool-calling and self-directed multi-step work. 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. Choose Kimi K2.5 if you need self-hosting or data privacy; Muse Spark 1.1 if you want a managed API.
Kimi K2.5 (Moonshot AI, China) and Muse Spark 1.1 (Meta, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Kimi K2.5 is moonshot's open-weight multimodal model at $0.60/$2.50 with a 256K window — strong visual coding and agentic work, since superseded by K2.6/K2.7. 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, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Price: Kimi K2.5 is about 2.1× cheaper on input ($0.6/$2.5 per 1M tokens vs $1.25/$4.25 per 1M tokens) — meaningful once you are processing millions of tokens a month.
Context window: Muse Spark 1.1 holds 4× more — 1M (~1,573 pages) vs 256K (~393 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 5 months (released July 9, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
Kimi K2.5
Muse Spark 1.1
Provider
Moonshot AI (China)
Meta (US)
Released
January 27, 2026
July 9, 2026
Context window
256K (~393 pages)
1M (~1,573 pages)
Price (in/out)
$0.6/$2.5 per 1M tokens
$1.25/$4.25 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, image, code
text, image, video, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
54.1%
Who wins what
Native multimodal reasoning and visual coding: Kimi K2.5 — 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
Agentic tool-calling and self-directed multi-step work: Kimi K2.5 — Moonshot's open-weight multimodal model at $0.60/$2.50 with a 256K window — strong visual coding and agentic work, since superseded by K2.6/K2.7 — and it runs cheaper at $0.6/$2.5 per 1M tokens.
Open-weight (Modified-MIT) — self-hostable at 256K context: Kimi K2.5 — Open weights make this possible at all — Muse Spark 1.1 is API-only, so it cannot leave the vendor's servers.
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; Kimi K2.5 does not.
Lowest cost at scale: Kimi K2.5 — At $0.6/$2.5 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 4× larger than Kimi K2.5's 256K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Kimi K2.5 — At $0.6/$2.5 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.
A team with data-privacy or self-hosting needs: Kimi K2.5 — Open weights let you run it on your own hardware; Muse Spark 1.1 is API-only.
Anyone whose priority is native multimodal reasoning and visual coding: Kimi K2.5 — 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.
An enterprise with regional data-residency rules: Muse Spark 1.1 or Kimi K2.5 — Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Kimi K2.5: where it fits
Moonshot's open-weight multimodal model at $0.60/$2.50 with a 256K window — strong visual coding and agentic work, since superseded by K2.6/K2.7. Released January 27, 2026 by Moonshot AI, it is built for native multimodal reasoning and visual coding, agentic tool-calling and self-directed multi-step work, open-weight (Modified-MIT) — self-hostable at 256K context, and vendor reports around 76.8% on its own SWE-agent coding harness.
Its trade-offs are real: its coding score uses Moonshot's own harness, not the standard SWE-Bench Verified protocol, superseded within Moonshot's line by Kimi K2.6 and K2.7, openRouter shows a promo price below Moonshot's $0.60/$2.50 list, and image input but no audio or video. At $0.6 in / $2.5 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
The defining split here is open vs. closed. Kimi K2.5 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Muse Spark 1.1 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 Kimi K2.5 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, Kimi K2.5 leans toward native multimodal reasoning and visual coding 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, Kimi K2.5 or Muse Spark 1.1?
Kimi K2.5 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Muse Spark 1.1 is API-metered at $1.25/$4.25 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 1.1 — 1M vs 256K, about 4× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Kimi K2.5 and Muse Spark 1.1 together?
Yes — a multi-model platform like LumiChats gives you Kimi K2.5, 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, Kimi K2.5 or Muse Spark 1.1?
Muse Spark 1.1 — released July 9, 2026, about 5 months after Kimi K2.5.
Kimi K2.5 vs Muse Spark 1.1
Moonshot AI · China | Meta · US · Updated June 2026
Quick verdict
Pick Kimi K2.5 for native multimodal reasoning and visual coding or agentic tool-calling and self-directed multi-step work. 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. Choose Kimi K2.5 if you need self-hosting or data privacy; Muse Spark 1.1 if you want a managed API.
Kimi K2.5 (Moonshot AI, China) and Muse Spark 1.1 (Meta, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Kimi K2.5 is moonshot's open-weight multimodal model at $0.60/$2.50 with a 256K window — strong visual coding and agentic work, since superseded by K2.6/K2.7. 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, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Price: Kimi K2.5 is about 2.1× cheaper on input ($0.6/$2.5 per 1M tokens vs $1.25/$4.25 per 1M tokens) — meaningful once you are processing millions of tokens a month.
▸Context window: Muse Spark 1.1 holds 4× more — 1M (~1,573 pages) vs 256K (~393 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 5 months (released July 9, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
Kimi K2.5
Muse Spark 1.1
Provider
Moonshot AI (China)
Meta (US)
Released
January 27, 2026
July 9, 2026
Context window
256K (~393 pages)
1M (~1,573 pages)
Price (in/out)
$0.6/$2.5 per 1M tokens
$1.25/$4.25 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, image, code
text, image, video, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
54.1%
Who wins what
Native multimodal reasoning and visual coding
Kimi K2.5
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
Agentic tool-calling and self-directed multi-step work
Kimi K2.5
Moonshot's open-weight multimodal model at $0.60/$2.50 with a 256K window — strong visual coding and agentic work, since superseded by K2.6/K2.7 — and it runs cheaper at $0.6/$2.5 per 1M tokens.
Open-weight (Modified-MIT) — self-hostable at 256K context
Kimi K2.5
Open weights make this possible at all — Muse Spark 1.1 is API-only, so it cannot leave the vendor's servers.
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; Kimi K2.5 does not.
Lowest cost at scale
Kimi K2.5
At $0.6/$2.5 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 4× larger than Kimi K2.5's 256K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Kimi K2.5
At $0.6/$2.5 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.
A team with data-privacy or self-hosting needs
→ Kimi K2.5
Open weights let you run it on your own hardware; Muse Spark 1.1 is API-only.
Anyone whose priority is native multimodal reasoning and visual coding
→ Kimi K2.5
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.
An enterprise with regional data-residency rules
→ Muse Spark 1.1 or Kimi K2.5
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Kimi K2.5: where it fits
Moonshot's open-weight multimodal model at $0.60/$2.50 with a 256K window — strong visual coding and agentic work, since superseded by K2.6/K2.7. Released January 27, 2026 by Moonshot AI, it is built for native multimodal reasoning and visual coding, agentic tool-calling and self-directed multi-step work, open-weight (Modified-MIT) — self-hostable at 256K context, and vendor reports around 76.8% on its own SWE-agent coding harness.
Its trade-offs are real: its coding score uses Moonshot's own harness, not the standard SWE-Bench Verified protocol, superseded within Moonshot's line by Kimi K2.6 and K2.7, openRouter shows a promo price below Moonshot's $0.60/$2.50 list, and image input but no audio or video. At $0.6 in / $2.5 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
The defining split here is open vs. closed. Kimi K2.5 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Muse Spark 1.1 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 Kimi K2.5 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.
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, Kimi K2.5 leans toward native multimodal reasoning and visual coding 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, Kimi K2.5 or Muse Spark 1.1?
Kimi K2.5 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Muse Spark 1.1 is API-metered at $1.25/$4.25 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 1.1 — 1M vs 256K, about 4× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Kimi K2.5 and Muse Spark 1.1 together?
Yes — a multi-model platform like LumiChats gives you Kimi K2.5, 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, Kimi K2.5 or Muse Spark 1.1?
Muse Spark 1.1 — released July 9, 2026, about 5 months after Kimi K2.5.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.