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 Qwen 3.7 Max for long-horizon agentic coding (swe-bench pro 60.6, terminal-bench 2.0 69.7) or 1m-token long-document and full-codebase analysis. On a tight budget at scale, ERNIE 5.0 is the value pick.
ERNIE 5.0 (Baidu) and Qwen 3.7 Max (Alibaba) 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. Qwen 3.7 Max is alibaba's agent-first frontier model — a 1M-token context and long-horizon coding at about half the cost of US flagships. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: ERNIE 5.0 is about 4.2× cheaper on input ($0.6/$2.1 per 1M tokens vs $2.5/$7.5 per 1M tokens) — meaningful once you are processing millions of tokens a month.
Context window: Qwen 3.7 Max holds 7.8× more — 1M (~1,500 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: Qwen 3.7 Max is the newer model by about 4 months (released May 20, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
ERNIE 5.0
Qwen 3.7 Max
Provider
Baidu (China)
Alibaba (China)
Released
January 22, 2026
May 20, 2026
Context window
128K (~192 pages)
1M (~1,500 pages)
Price (in/out)
$0.6/$2.1 per 1M tokens
$2.5/$7.5 per 1M tokens
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 — Qwen 3.7 Max is comparatively weak here — text-only — no vision input (the Plus variant adds images)
Particularly strong on Chinese-language reasoning tasks: ERNIE 5.0 — Qwen 3.7 Max is comparatively weak here — trails GPT-5.5 and Claude Opus on the hardest one-shot reasoning
Competitive API pricing (around $0.60/$2.10 per million tokens): ERNIE 5.0 — 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 — and it runs cheaper at $0.6/$2.1 per 1M tokens.
Long-horizon agentic coding (SWE-Bench Pro 60.6, Terminal-Bench 2.0 69.7): Qwen 3.7 Max — Its 1M window holds about 7.8× more than ERNIE 5.0's 128K in a single prompt.
1M-token long-document and full-codebase analysis: Qwen 3.7 Max — ERNIE 5.0 is comparatively weak here — 128K context is smaller than 1M-token rivals
MCP tool orchestration and multi-hour autonomous runs: Qwen 3.7 Max — Alibaba's agent-first frontier model — a 1M-token context and long-horizon coding at about half the cost of US flagships — and it carries the larger 1M context.
Lowest cost at scale: ERNIE 5.0 — At $0.6/$2.1 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Qwen 3.7 Max — Its 1M window is about 7.8× larger than ERNIE 5.0's 128K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: ERNIE 5.0 — At $0.6/$2.1 per 1M tokens it undercuts Qwen 3.7 Max, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Qwen 3.7 Max — Larger 1M 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 long-horizon agentic coding (swe-bench pro 60.6, terminal-bench 2.0 69.7): Qwen 3.7 Max — 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.
Qwen 3.7 Max: where it fits
Alibaba's agent-first frontier model — a 1M-token context and long-horizon coding at about half the cost of US flagships. Released May 20, 2026 by Alibaba, it is built for long-horizon agentic coding (SWE-Bench Pro 60.6, Terminal-Bench 2.0 69.7), 1M-token long-document and full-codebase analysis, mCP tool orchestration and multi-hour autonomous runs, and frontier intelligence at roughly half the price of US flagships.
Its trade-offs: text-only — no vision input (the Plus variant adds images), closed-weight, API-only — no self-hosting, trails GPT-5.5 and Claude Opus on the hardest one-shot reasoning, and chinese-jurisdiction data-residency considerations. At $2.5 in / $7.5 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
ERNIE 5.0 and Qwen 3.7 Max overlap enough that the right pick depends on your specific job. ERNIE 5.0 costs less per token; Qwen 3.7 Max holds the larger context; and each leads in its own area — ERNIE 5.0 for baidu's flagship omni-modal model — text, image and video understanding, Qwen 3.7 Max for long-horizon agentic coding (swe-bench pro 60.6, terminal-bench 2.0 69.7). Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is ERNIE 5.0 or Qwen 3.7 Max 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 Qwen 3.7 Max leans toward long-horizon agentic coding (swe-bench pro 60.6, terminal-bench 2.0 69.7), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, ERNIE 5.0 or Qwen 3.7 Max?
ERNIE 5.0 is cheaper — $0.6/$2.1 per 1M tokens vs $2.5/$7.5 per 1M tokens, roughly 4.2× apart on input.
Which has the bigger context window?
Qwen 3.7 Max — 1M 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 ERNIE 5.0 and Qwen 3.7 Max together?
Yes — a multi-model platform like LumiChats gives you ERNIE 5.0, Qwen 3.7 Max 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 Qwen 3.7 Max?
Qwen 3.7 Max — released May 20, 2026, about 4 months after ERNIE 5.0.
ERNIE 5.0 vs Qwen 3.7 Max
Baidu · China | Alibaba · 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 Qwen 3.7 Max for long-horizon agentic coding (swe-bench pro 60.6, terminal-bench 2.0 69.7) or 1m-token long-document and full-codebase analysis. On a tight budget at scale, ERNIE 5.0 is the value pick.
ERNIE 5.0 (Baidu) and Qwen 3.7 Max (Alibaba) 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. Qwen 3.7 Max is alibaba's agent-first frontier model — a 1M-token context and long-horizon coding at about half the cost of US flagships. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: ERNIE 5.0 is about 4.2× cheaper on input ($0.6/$2.1 per 1M tokens vs $2.5/$7.5 per 1M tokens) — meaningful once you are processing millions of tokens a month.
▸Context window: Qwen 3.7 Max holds 7.8× more — 1M (~1,500 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: Qwen 3.7 Max is the newer model by about 4 months (released May 20, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
ERNIE 5.0
Qwen 3.7 Max
Provider
Baidu (China)
Alibaba (China)
Released
January 22, 2026
May 20, 2026
Context window
128K (~192 pages)
1M (~1,500 pages)
Price (in/out)
$0.6/$2.1 per 1M tokens
$2.5/$7.5 per 1M tokens
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
Qwen 3.7 Max is comparatively weak here — text-only — no vision input (the Plus variant adds images)
Particularly strong on Chinese-language reasoning tasks
ERNIE 5.0
Qwen 3.7 Max is comparatively weak here — trails GPT-5.5 and Claude Opus on the hardest one-shot reasoning
Competitive API pricing (around $0.60/$2.10 per million tokens)
ERNIE 5.0
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 — and it runs cheaper at $0.6/$2.1 per 1M tokens.
Long-horizon agentic coding (SWE-Bench Pro 60.6, Terminal-Bench 2.0 69.7)
Qwen 3.7 Max
Its 1M window holds about 7.8× more than ERNIE 5.0's 128K in a single prompt.
1M-token long-document and full-codebase analysis
Qwen 3.7 Max
ERNIE 5.0 is comparatively weak here — 128K context is smaller than 1M-token rivals
MCP tool orchestration and multi-hour autonomous runs
Qwen 3.7 Max
Alibaba's agent-first frontier model — a 1M-token context and long-horizon coding at about half the cost of US flagships — and it carries the larger 1M context.
Lowest cost at scale
ERNIE 5.0
At $0.6/$2.1 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Qwen 3.7 Max
Its 1M window is about 7.8× larger than ERNIE 5.0's 128K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ ERNIE 5.0
At $0.6/$2.1 per 1M tokens it undercuts Qwen 3.7 Max, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Qwen 3.7 Max
Larger 1M 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 long-horizon agentic coding (swe-bench pro 60.6, terminal-bench 2.0 69.7)
→ Qwen 3.7 Max
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.
Qwen 3.7 Max: where it fits
Alibaba's agent-first frontier model — a 1M-token context and long-horizon coding at about half the cost of US flagships. Released May 20, 2026 by Alibaba, it is built for long-horizon agentic coding (SWE-Bench Pro 60.6, Terminal-Bench 2.0 69.7), 1M-token long-document and full-codebase analysis, mCP tool orchestration and multi-hour autonomous runs, and frontier intelligence at roughly half the price of US flagships.
Its trade-offs: text-only — no vision input (the Plus variant adds images), closed-weight, API-only — no self-hosting, trails GPT-5.5 and Claude Opus on the hardest one-shot reasoning, and chinese-jurisdiction data-residency considerations. At $2.5 in / $7.5 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
ERNIE 5.0 and Qwen 3.7 Max overlap enough that the right pick depends on your specific job. ERNIE 5.0 costs less per token; Qwen 3.7 Max holds the larger context; and each leads in its own area — ERNIE 5.0 for baidu's flagship omni-modal model — text, image and video understanding, Qwen 3.7 Max for long-horizon agentic coding (swe-bench pro 60.6, terminal-bench 2.0 69.7). Rather than crowning one, run the same hard task through both once and let the results decide.
Want both ERNIE 5.0 and Qwen 3.7 Max 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 Qwen 3.7 Max leans toward long-horizon agentic coding (swe-bench pro 60.6, terminal-bench 2.0 69.7), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, ERNIE 5.0 or Qwen 3.7 Max?
ERNIE 5.0 is cheaper — $0.6/$2.1 per 1M tokens vs $2.5/$7.5 per 1M tokens, roughly 4.2× apart on input.
Which has the bigger context window?
Qwen 3.7 Max — 1M 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 ERNIE 5.0 and Qwen 3.7 Max together?
Yes — a multi-model platform like LumiChats gives you ERNIE 5.0, Qwen 3.7 Max 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 Qwen 3.7 Max?
Qwen 3.7 Max — released May 20, 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.