Pick ERNIE 5.0 for baidu's flagship omni-modal model — text, image and video understanding or particularly strong on chinese-language reasoning tasks. Pick Kimi K2.5 for native multimodal reasoning and visual coding or agentic tool-calling and self-directed multi-step work. Choose Kimi K2.5 if you need self-hosting or data privacy; ERNIE 5.0 if you want a managed API.
ERNIE 5.0 (Baidu) and Kimi K2.5 (Moonshot AI) are two of the models people most often weigh against each other in 2026. ERNIE 5.0 is baidu's flagship omni-modal ERNIE model — strong Chinese-language and multimodal reasoning at low cost, though it trails the Western frontier on independent benchmarks. 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open-weight multimodal model at $0.60/$2.50 with a 256K window — strong visual coding and agentic work, since superseded by K2.6/K2.7. They diverge most on context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Cost model: Kimi K2.5 ships open weights you can self-host (hardware cost only, no per-token fee), while ERNIE 5.0 is API-metered at $0.6/$2.1 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
Context window: Kimi K2.5 holds 2× more — 256K (~393 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.
Specifications
Spec
ERNIE 5.0
Kimi K2.5
Provider
Baidu (China)
Moonshot AI (China)
Released
January 22, 2026
January 27, 2026
Context window
128K (~192 pages)
256K (~393 pages)
Price (in/out)
$0.6/$2.1 per 1M tokens
$0.6/$2.5 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, video, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Baidu's flagship omni-modal model — text, image and video understanding: ERNIE 5.0 — Kimi K2.5 is comparatively weak here — image input but no audio or video
Particularly strong on Chinese-language reasoning tasks: ERNIE 5.0 — ERNIE 5.0 lists particularly strong on Chinese-language reasoning tasks among its strengths; Kimi K2.5 does not.
Competitive API pricing (around $0.60/$2.10 per million tokens): ERNIE 5.0 — ERNIE 5.0 lists competitive API pricing (around $0.60/$2.10 per million tokens) among its strengths; Kimi K2.5 does not.
Native multimodal reasoning and visual coding: 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 carries the larger 256K context.
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 its weights are open while ERNIE 5.0 is API-only.
Open-weight (Modified-MIT) — self-hostable at 256K context: Kimi K2.5 — Its 256K window holds about 2× more than ERNIE 5.0's 128K in a single prompt.
Largest single-prompt input: Kimi K2.5 — Its 256K window is about 2× larger than ERNIE 5.0's 128K, fitting roughly 393 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases: Kimi K2.5 — Larger 256K 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; ERNIE 5.0 is API-only.
Anyone whose priority is baidu's flagship omni-modal model — text, image and video understanding: ERNIE 5.0 — It is specifically built for that.
Anyone whose priority is native multimodal reasoning and visual coding: Kimi K2.5 — That is its strongest area.
ERNIE 5.0: where it fits
Baidu's flagship omni-modal ERNIE model — strong Chinese-language and multimodal reasoning at low cost, though it trails the Western frontier on independent benchmarks. Released January 22, 2026 by Baidu, it is built for baidu's flagship omni-modal model — text, image and video understanding, particularly strong on Chinese-language reasoning tasks, competitive API pricing (around $0.60/$2.10 per million tokens), and backed by a major lab with deep China-market integration.
Its trade-offs are real: trails the Western frontier on aggregate independent tests (AA Intelligence Index ~22 for the tracked Thinking Preview), parameter and architecture details are vendor-stated and opaque, closed weights on a China-hosted API, and 128K context is smaller than 1M-token rivals. At $0.6 in / $2.1 out per million tokens, it sits in the budget price band.
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: 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.
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. ERNIE 5.0 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 ERNIE 5.0 or Kimi K2.5 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, ERNIE 5.0 leans toward baidu's flagship omni-modal model — text, image and video understanding while Kimi K2.5 leans toward native multimodal reasoning and visual coding, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, ERNIE 5.0 or Kimi K2.5?
Kimi K2.5 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while ERNIE 5.0 is API-metered at $0.6/$2.1 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?
Kimi K2.5 — 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 ERNIE 5.0 and Kimi K2.5 together?
Yes — a multi-model platform like LumiChats gives you ERNIE 5.0, Kimi K2.5 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, ERNIE 5.0 or Kimi K2.5?
Kimi K2.5 — released January 27, 2026, about 5 days after ERNIE 5.0.
ERNIE 5.0 vs Kimi K2.5
Baidu · China | Moonshot AI · China · Updated June 2026
Quick verdict
Pick ERNIE 5.0 for baidu's flagship omni-modal model — text, image and video understanding or particularly strong on chinese-language reasoning tasks. Pick Kimi K2.5 for native multimodal reasoning and visual coding or agentic tool-calling and self-directed multi-step work. Choose Kimi K2.5 if you need self-hosting or data privacy; ERNIE 5.0 if you want a managed API.
ERNIE 5.0 (Baidu) and Kimi K2.5 (Moonshot AI) are two of the models people most often weigh against each other in 2026. ERNIE 5.0 is baidu's flagship omni-modal ERNIE model — strong Chinese-language and multimodal reasoning at low cost, though it trails the Western frontier on independent benchmarks. 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. They diverge most on context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Cost model: Kimi K2.5 ships open weights you can self-host (hardware cost only, no per-token fee), while ERNIE 5.0 is API-metered at $0.6/$2.1 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
▸Context window: Kimi K2.5 holds 2× more — 256K (~393 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.
Side-by-side specs
Spec
ERNIE 5.0
Kimi K2.5
Provider
Baidu (China)
Moonshot AI (China)
Released
January 22, 2026
January 27, 2026
Context window
128K (~192 pages)
256K (~393 pages)
Price (in/out)
$0.6/$2.1 per 1M tokens
$0.6/$2.5 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, video, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Baidu's flagship omni-modal model — text, image and video understanding
ERNIE 5.0
Kimi K2.5 is comparatively weak here — image input but no audio or video
Particularly strong on Chinese-language reasoning tasks
ERNIE 5.0
ERNIE 5.0 lists particularly strong on Chinese-language reasoning tasks among its strengths; Kimi K2.5 does not.
Competitive API pricing (around $0.60/$2.10 per million tokens)
ERNIE 5.0
ERNIE 5.0 lists competitive API pricing (around $0.60/$2.10 per million tokens) among its strengths; Kimi K2.5 does not.
Native multimodal reasoning and visual coding
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 carries the larger 256K context.
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 its weights are open while ERNIE 5.0 is API-only.
Open-weight (Modified-MIT) — self-hostable at 256K context
Kimi K2.5
Its 256K window holds about 2× more than ERNIE 5.0's 128K in a single prompt.
Largest single-prompt input
Kimi K2.5
Its 256K window is about 2× larger than ERNIE 5.0's 128K, fitting roughly 393 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases
→ Kimi K2.5
Larger 256K 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; ERNIE 5.0 is API-only.
Anyone whose priority is baidu's flagship omni-modal model — text, image and video understanding
→ ERNIE 5.0
It is specifically built for that.
Anyone whose priority is native multimodal reasoning and visual coding
→ Kimi K2.5
That is its strongest area.
ERNIE 5.0: where it fits
Baidu's flagship omni-modal ERNIE model — strong Chinese-language and multimodal reasoning at low cost, though it trails the Western frontier on independent benchmarks. Released January 22, 2026 by Baidu, it is built for baidu's flagship omni-modal model — text, image and video understanding, particularly strong on Chinese-language reasoning tasks, competitive API pricing (around $0.60/$2.10 per million tokens), and backed by a major lab with deep China-market integration.
Its trade-offs are real: trails the Western frontier on aggregate independent tests (AA Intelligence Index ~22 for the tracked Thinking Preview), parameter and architecture details are vendor-stated and opaque, closed weights on a China-hosted API, and 128K context is smaller than 1M-token rivals. At $0.6 in / $2.1 out per million tokens, it sits in the budget price band.
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: 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.
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. ERNIE 5.0 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 ERNIE 5.0 and Kimi K2.5 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, ERNIE 5.0 leans toward baidu's flagship omni-modal model — text, image and video understanding while Kimi K2.5 leans toward native multimodal reasoning and visual coding, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, ERNIE 5.0 or Kimi K2.5?
Kimi K2.5 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while ERNIE 5.0 is API-metered at $0.6/$2.1 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?
Kimi K2.5 — 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 ERNIE 5.0 and Kimi K2.5 together?
Yes — a multi-model platform like LumiChats gives you ERNIE 5.0, Kimi K2.5 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, ERNIE 5.0 or Kimi K2.5?
Kimi K2.5 — released January 27, 2026, about 5 days after ERNIE 5.0.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.