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 MAI-Thinking-1 for very strong math reasoning (aime 2025 97%, aime 2026 94.5%) or microsoft's first in-house flagship reasoner, trained without openai distillation. On a tight budget at scale, MAI-Thinking-1 is the value pick.
ERNIE 5.0 (Baidu, China) and MAI-Thinking-1 (Microsoft, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. MAI-Thinking-1 is microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Context window: MAI-Thinking-1 holds 2× more — 256K (~384 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: MAI-Thinking-1 is the newer model by about 4 months (released June 2, 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
ERNIE 5.0
MAI-Thinking-1
Provider
Baidu (China)
Microsoft (US)
Released
January 22, 2026
June 2, 2026
Context window
128K (~192 pages)
256K (~384 pages)
Price (in/out)
$0.6/$2.1 per 1M tokens
Not published
Open weight?
No — API only
No — API only
Modalities
text, image, video, code
text, 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 — ERNIE 5.0 lists baidu's flagship omni-modal model — text, image and video understanding among its strengths; MAI-Thinking-1 does not.
Particularly strong on Chinese-language reasoning tasks: ERNIE 5.0 — ERNIE 5.0 lists particularly strong on Chinese-language reasoning tasks among its strengths; MAI-Thinking-1 does not.
Competitive API pricing (around $0.60/$2.10 per million tokens): ERNIE 5.0 — MAI-Thinking-1 is comparatively weak here — closed and in private preview — no open weights, no published pricing, thin availability
Very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%): MAI-Thinking-1 — Microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence — and it carries the larger 256K context.
Microsoft's first in-house flagship reasoner, trained without OpenAI distillation: MAI-Thinking-1 — Microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence — and it is the newer of the two.
Efficient reasoning at low token cost for its class: MAI-Thinking-1 — Its 256K window holds about 2× more than ERNIE 5.0's 128K in a single prompt.
Lowest cost at scale: MAI-Thinking-1 — Its weights are open, so at volume you pay for your own hardware instead of ERNIE 5.0's $0.6/$2.1 per 1M tokens.
Largest single-prompt input: MAI-Thinking-1 — Its 256K window is about 2× larger than ERNIE 5.0's 128K, fitting roughly 384 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: MAI-Thinking-1 — At Not published it undercuts ERNIE 5.0, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: MAI-Thinking-1 — Larger 256K window fits more in one prompt.
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 very strong math reasoning (aime 2025 97%, aime 2026 94.5%): MAI-Thinking-1 — That is its strongest area.
An enterprise with regional data-residency rules: MAI-Thinking-1 or ERNIE 5.0 — Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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.
MAI-Thinking-1: where it fits
Microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence. Released June 2, 2026 by Microsoft, it is built for very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%), microsoft's first in-house flagship reasoner, trained without OpenAI distillation, efficient reasoning at low token cost for its class, and competitive with Claude Opus 4.6 on SWE-Bench Pro (vendor-reported).
Its trade-offs: closed and in private preview — no open weights, no published pricing, thin availability, and benchmarks are largely self-reported.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." ERNIE 5.0 (China) and MAI-Thinking-1 (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. MAI-Thinking-1 is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Frequently asked questions
Is ERNIE 5.0 or MAI-Thinking-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, ERNIE 5.0 leans toward baidu's flagship omni-modal model — text, image and video understanding while MAI-Thinking-1 leans toward very strong math reasoning (aime 2025 97%, aime 2026 94.5%), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, ERNIE 5.0 or MAI-Thinking-1?
MAI-Thinking-1 is cheaper — $0.6/$2.1 per 1M tokens vs Not published.
Which has the bigger context window?
MAI-Thinking-1 — 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 MAI-Thinking-1 together?
Yes — a multi-model platform like LumiChats gives you ERNIE 5.0, MAI-Thinking-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, ERNIE 5.0 or MAI-Thinking-1?
MAI-Thinking-1 — released June 2, 2026, about 4 months after ERNIE 5.0.
ERNIE 5.0 vs MAI-Thinking-1
Baidu · China | Microsoft · US · 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 MAI-Thinking-1 for very strong math reasoning (aime 2025 97%, aime 2026 94.5%) or microsoft's first in-house flagship reasoner, trained without openai distillation. On a tight budget at scale, MAI-Thinking-1 is the value pick.
ERNIE 5.0 (Baidu, China) and MAI-Thinking-1 (Microsoft, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. MAI-Thinking-1 is microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Context window: MAI-Thinking-1 holds 2× more — 256K (~384 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: MAI-Thinking-1 is the newer model by about 4 months (released June 2, 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
ERNIE 5.0
MAI-Thinking-1
Provider
Baidu (China)
Microsoft (US)
Released
January 22, 2026
June 2, 2026
Context window
128K (~192 pages)
256K (~384 pages)
Price (in/out)
$0.6/$2.1 per 1M tokens
Not published
Open weight?
No — API only
No — API only
Modalities
text, image, video, code
text, 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
ERNIE 5.0 lists baidu's flagship omni-modal model — text, image and video understanding among its strengths; MAI-Thinking-1 does not.
Particularly strong on Chinese-language reasoning tasks
ERNIE 5.0
ERNIE 5.0 lists particularly strong on Chinese-language reasoning tasks among its strengths; MAI-Thinking-1 does not.
Competitive API pricing (around $0.60/$2.10 per million tokens)
ERNIE 5.0
MAI-Thinking-1 is comparatively weak here — closed and in private preview — no open weights, no published pricing, thin availability
Very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%)
MAI-Thinking-1
Microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence — and it carries the larger 256K context.
Microsoft's first in-house flagship reasoner, trained without OpenAI distillation
MAI-Thinking-1
Microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence — and it is the newer of the two.
Efficient reasoning at low token cost for its class
MAI-Thinking-1
Its 256K window holds about 2× more than ERNIE 5.0's 128K in a single prompt.
Lowest cost at scale
MAI-Thinking-1
Its weights are open, so at volume you pay for your own hardware instead of ERNIE 5.0's $0.6/$2.1 per 1M tokens.
Largest single-prompt input
MAI-Thinking-1
Its 256K window is about 2× larger than ERNIE 5.0's 128K, fitting roughly 384 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ MAI-Thinking-1
At Not published it undercuts ERNIE 5.0, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ MAI-Thinking-1
Larger 256K window fits more in one prompt.
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 very strong math reasoning (aime 2025 97%, aime 2026 94.5%)
→ MAI-Thinking-1
That is its strongest area.
An enterprise with regional data-residency rules
→ MAI-Thinking-1 or ERNIE 5.0
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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.
MAI-Thinking-1: where it fits
Microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence. Released June 2, 2026 by Microsoft, it is built for very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%), microsoft's first in-house flagship reasoner, trained without OpenAI distillation, efficient reasoning at low token cost for its class, and competitive with Claude Opus 4.6 on SWE-Bench Pro (vendor-reported).
Its trade-offs: closed and in private preview — no open weights, no published pricing, thin availability, and benchmarks are largely self-reported.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." ERNIE 5.0 (China) and MAI-Thinking-1 (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. MAI-Thinking-1 is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Want both ERNIE 5.0 and MAI-Thinking-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, ERNIE 5.0 leans toward baidu's flagship omni-modal model — text, image and video understanding while MAI-Thinking-1 leans toward very strong math reasoning (aime 2025 97%, aime 2026 94.5%), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, ERNIE 5.0 or MAI-Thinking-1?
MAI-Thinking-1 is cheaper — $0.6/$2.1 per 1M tokens vs Not published.
Which has the bigger context window?
MAI-Thinking-1 — 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 MAI-Thinking-1 together?
Yes — a multi-model platform like LumiChats gives you ERNIE 5.0, MAI-Thinking-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, ERNIE 5.0 or MAI-Thinking-1?
MAI-Thinking-1 — released June 2, 2026, about 4 months 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.