Pick DeepSeek V4 for near-frontier coding at ~1/12 the cost or open mit-licensed weights you can self-host. Pick ERNIE 5.0 for baidu's flagship omni-modal model — text, image and video understanding or particularly strong on chinese-language reasoning tasks. Choose DeepSeek V4 if you need self-hosting or data privacy; ERNIE 5.0 if you want a managed API.
DeepSeek V4 (DeepSeek) and ERNIE 5.0 (Baidu) are two of the models people most often weigh against each other in 2026. DeepSeek V4 is china's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. 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. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Price: DeepSeek V4 is about 1.4× cheaper on input ($0.435/$0.87 per 1M tokens vs $0.6/$2.1 per 1M tokens) — modest, but it adds up at steady volume.
Context window: DeepSeek V4 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: DeepSeek V4 is the newer model by about 3 months (released April 24, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
DeepSeek V4
ERNIE 5.0
Provider
DeepSeek (China)
Baidu (China)
Released
April 24, 2026
January 22, 2026
Context window
1M (~1,500 pages)
128K (~192 pages)
Price (in/out)
$0.435/$0.87 per 1M tokens
$0.6/$2.1 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, code
text, image, video, code
SWE-Bench Verified
80.6%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Near-frontier coding at ~1/12 the cost: DeepSeek V4 — At $0.435/$0.87 per 1M tokens it undercuts ERNIE 5.0 ($0.6/$2.1 per 1M tokens), and that gap compounds at volume.
Open MIT-licensed weights you can self-host: DeepSeek V4 — Open weights make this possible at all — ERNIE 5.0 is API-only, so it cannot leave the vendor's servers.
No long-context surcharge: DeepSeek V4 — Its 1M window holds about 7.8× more than ERNIE 5.0's 128K in a single prompt.
Baidu's flagship omni-modal model — text, image and video understanding: ERNIE 5.0 — DeepSeek V4 is comparatively weak here — text/code focused, less multimodal
Particularly strong on Chinese-language reasoning tasks: ERNIE 5.0 — ERNIE 5.0 lists particularly strong on Chinese-language reasoning tasks among its strengths; DeepSeek V4 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; DeepSeek V4 does not.
Lowest cost at scale: DeepSeek V4 — At $0.435/$0.87 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: DeepSeek V4 — 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: DeepSeek V4 — At $0.435/$0.87 per 1M tokens it undercuts ERNIE 5.0, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: DeepSeek V4 — Larger 1M window fits more in one prompt.
A team with data-privacy or self-hosting needs: DeepSeek V4 — Open weights let you run it on your own hardware; ERNIE 5.0 is API-only.
Anyone whose priority is near-frontier coding at ~1/12 the cost: DeepSeek V4 — It is specifically built for that.
Anyone whose priority is baidu's flagship omni-modal model — text, image and video understanding: ERNIE 5.0 — That is its strongest area.
DeepSeek V4: where it fits
China's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. Released April 24, 2026 by DeepSeek, it is built for near-frontier coding at ~1/12 the cost, open MIT-licensed weights you can self-host, no long-context surcharge, and highest LiveCodeBench result.
Its trade-offs are real: trails the very best on hardest agentic coding, and text/code focused, less multimodal. At $0.435 in / $0.87 out per million tokens, it sits in the budget price band.
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: 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.
The bottom line for this matchup
The defining split here is open vs. closed. DeepSeek V4 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 DeepSeek V4 or ERNIE 5.0 better for coding?
Public SWE-Bench figures are not available for ERNIE 5.0, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek V4 leans toward near-frontier coding at ~1/12 the cost while ERNIE 5.0 leans toward baidu's flagship omni-modal model — text, image and video understanding, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V4 or ERNIE 5.0?
DeepSeek V4 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?
DeepSeek V4 — 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 DeepSeek V4 and ERNIE 5.0 together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4, ERNIE 5.0 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, DeepSeek V4 or ERNIE 5.0?
DeepSeek V4 — released April 24, 2026, about 3 months after ERNIE 5.0.
DeepSeek V4 vs ERNIE 5.0
DeepSeek · China | Baidu · China · Updated June 2026
Quick verdict
Pick DeepSeek V4 for near-frontier coding at ~1/12 the cost or open mit-licensed weights you can self-host. Pick ERNIE 5.0 for baidu's flagship omni-modal model — text, image and video understanding or particularly strong on chinese-language reasoning tasks. Choose DeepSeek V4 if you need self-hosting or data privacy; ERNIE 5.0 if you want a managed API.
DeepSeek V4 (DeepSeek) and ERNIE 5.0 (Baidu) are two of the models people most often weigh against each other in 2026. DeepSeek V4 is china's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. 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. 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: DeepSeek V4 is about 1.4× cheaper on input ($0.435/$0.87 per 1M tokens vs $0.6/$2.1 per 1M tokens) — modest, but it adds up at steady volume.
▸Context window: DeepSeek V4 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: DeepSeek V4 is the newer model by about 3 months (released April 24, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
DeepSeek V4
ERNIE 5.0
Provider
DeepSeek (China)
Baidu (China)
Released
April 24, 2026
January 22, 2026
Context window
1M (~1,500 pages)
128K (~192 pages)
Price (in/out)
$0.435/$0.87 per 1M tokens
$0.6/$2.1 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, code
text, image, video, code
SWE-Bench Verified
80.6%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Near-frontier coding at ~1/12 the cost
DeepSeek V4
At $0.435/$0.87 per 1M tokens it undercuts ERNIE 5.0 ($0.6/$2.1 per 1M tokens), and that gap compounds at volume.
Open MIT-licensed weights you can self-host
DeepSeek V4
Open weights make this possible at all — ERNIE 5.0 is API-only, so it cannot leave the vendor's servers.
No long-context surcharge
DeepSeek V4
Its 1M window holds about 7.8× more than ERNIE 5.0's 128K in a single prompt.
Baidu's flagship omni-modal model — text, image and video understanding
ERNIE 5.0
DeepSeek V4 is comparatively weak here — text/code focused, less multimodal
Particularly strong on Chinese-language reasoning tasks
ERNIE 5.0
ERNIE 5.0 lists particularly strong on Chinese-language reasoning tasks among its strengths; DeepSeek V4 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; DeepSeek V4 does not.
Lowest cost at scale
DeepSeek V4
At $0.435/$0.87 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
DeepSeek V4
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
→ DeepSeek V4
At $0.435/$0.87 per 1M tokens it undercuts ERNIE 5.0, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ DeepSeek V4
Larger 1M window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ DeepSeek V4
Open weights let you run it on your own hardware; ERNIE 5.0 is API-only.
Anyone whose priority is near-frontier coding at ~1/12 the cost
→ DeepSeek V4
It is specifically built for that.
Anyone whose priority is baidu's flagship omni-modal model — text, image and video understanding
→ ERNIE 5.0
That is its strongest area.
DeepSeek V4: where it fits
China's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. Released April 24, 2026 by DeepSeek, it is built for near-frontier coding at ~1/12 the cost, open MIT-licensed weights you can self-host, no long-context surcharge, and highest LiveCodeBench result.
Its trade-offs are real: trails the very best on hardest agentic coding, and text/code focused, less multimodal. At $0.435 in / $0.87 out per million tokens, it sits in the budget price band.
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: 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.
The bottom line for this matchup
The defining split here is open vs. closed. DeepSeek V4 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 DeepSeek V4 and ERNIE 5.0 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 ERNIE 5.0, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek V4 leans toward near-frontier coding at ~1/12 the cost while ERNIE 5.0 leans toward baidu's flagship omni-modal model — text, image and video understanding, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V4 or ERNIE 5.0?
DeepSeek V4 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?
DeepSeek V4 — 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 DeepSeek V4 and ERNIE 5.0 together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4, ERNIE 5.0 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, DeepSeek V4 or ERNIE 5.0?
DeepSeek V4 — released April 24, 2026, about 3 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.