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DeepSeek V4 Pro 0813 vs Ornith-1.5-35B-A3B

Updated October 2, 2026. Rank says DeepSeek V4 Pro 0813 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

DeepSeek V4 Pro 0813 has the higher public point estimate, 64.98 versus 32.61. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 14 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
DeepSeek logo
DeepSeek V4 Pro 0813

DeepSeek

64.98/100

Estimated · Public rank #37

Conditional range 50.6–79.3

Model B
Ornith-1.5-35B-A3B

Ornith AI

32.61/100

Estimated · Public rank #152

Conditional range 18.3–47.0

Shared results
14
DeepSeek V4 Pro 0813 only
28
Ornith-1.5-35B-A3B only
4
Like-for-like categories
0 / 8
Estimated: DeepSeek V4 Pro 0813 and Ornith-1.5-35B-A3B. Conditional ranges do not establish rank confidence.How the comparison works

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Long documents

    Prompts that approach the documented context limit

    DeepSeek V4 Pro 0813

    DeepSeek V4 Pro 0813 has the larger documented context window.

    Confidence: documented
Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Ornith-1.5-35B-A3B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    Ornith-1.5-35B-A3B is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

49.2DeepSeek V4 Pro 081324.0Ornith-1.5-35B-A3B

Directional only · BenchAlign v5.8

DeepSeek V4 Pro 0813 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboardShortlist models for this job
Same basis rules as the category table below

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Directional only
DeepSeek V4 Pro 0813
52.7
Supported · #40/119
Ornith-1.5-35B-A3B
23.5
Estimated · #95/119
Basis
BenchAlign v5.8 lane · 11 vs 7 public rows
Reading
Directional only

Coding

Directional only
DeepSeek V4 Pro 0813
49.2
Supported · #46/144
Ornith-1.5-35B-A3B
24.0
Estimated · #110/144
Basis
BenchAlign v5.8 lane · 15 vs 7 public rows
Reading
Directional only

Knowledge

Directional only
DeepSeek V4 Pro 0813
63.7
Estimated · #34/171
Ornith-1.5-35B-A3B
34.5
Estimated · #119/171
Basis
BenchAlign v5.8 lane · 8 vs 4 public rows
Reading
Directional only

Reasoning

Not comparable
DeepSeek V4 Pro 0813
56.9
Unranked · 4 rankable rows
Ornith-1.5-35B-A3B
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V4 Pro 0813
Not ranked
Ornith-1.5-35B-A3B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V4 Pro 0813
Not ranked
Ornith-1.5-35B-A3B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V4 Pro 0813
Not ranked
Ornith-1.5-35B-A3B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
DeepSeek V4 Pro 0813
80.2
Unranked · 4 rankable rows
Ornith-1.5-35B-A3B
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

DeepSeek V4 Pro 0813
$0.0033
Fits in one request
Ornith-1.5-35B-A3B
Self-hosted; infrastructure cost varies
Fits in one request

Ornith-1.5-35B-A3B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

DeepSeek V4 Pro 0813
$0.07788
Fits in one request
Ornith-1.5-35B-A3B
Self-hosted; infrastructure cost varies
Fits in one request

Ornith-1.5-35B-A3B has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

DeepSeek V4 Pro 0813
$0.0748
Fits in one request
Ornith-1.5-35B-A3B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Ornith-1.5-35B-A3B has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Reasoning profile

DeepSeek V4 Pro 0813

Reasoning

Ornith-1.5-35B-A3B

Reasoning

Weight access

DeepSeek V4 Pro 0813

Open Weight

Ornith-1.5-35B-A3B

Open Weight

License

DeepSeek V4 Pro 0813

Open Weight

Ornith-1.5-35B-A3B

Open Weight

Release date

DeepSeek V4 Pro 0813

2026-08-13

Ornith-1.5-35B-A3B

2026-08-18

If you already use one of these models

Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
DeepSeek V4 Pro 0813 has the higher public point estimate, 64.98 versus 32.61. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
DeepSeek V4 Pro 0813 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, DeepSeek V4 Pro 0813 or Ornith-1.5-35B-A3B?

DeepSeek V4 Pro 0813 has the higher public point estimate, 64.98 versus 32.61. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, DeepSeek V4 Pro 0813 or Ornith-1.5-35B-A3B?

DeepSeek V4 Pro 0813 scores higher for coding on the public lane, 49.2 to 24. Ornith-1.5-35B-A3B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, DeepSeek V4 Pro 0813 or Ornith-1.5-35B-A3B?

DeepSeek V4 Pro 0813 scores higher for agentic tasks on the public lane, 52.7 to 23.5. Ornith-1.5-35B-A3B is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, DeepSeek V4 Pro 0813 or Ornith-1.5-35B-A3B?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, DeepSeek V4 Pro 0813 or Ornith-1.5-35B-A3B?

DeepSeek V4 Pro 0813 has the larger documented context window: 1M, compared with 262K.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence46 rows

Agentic

  • Terminal-Bench 2.0

    DeepSeek V4 Pro 081367.9%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek V4 Pro 081387.9%
    Source
    Ornith-1.5-35B-A3B67.8%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • BrowseComp

    DeepSeek V4 Pro 081383.4%
    Source
    Ornith-1.5-35B-A3B67.6%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • HLE w/ tools

    DeepSeek V4 Pro 081360.0%
    Source
    Ornith-1.5-35B-A3B33.4%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • MCP Atlas

    DeepSeek V4 Pro 081373.6%
    Source
    Ornith-1.5-35B-A3B70.2%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • Toolathlon

    DeepSeek V4 Pro 081351.8%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • CyberGym

    DeepSeek V4 Pro 081383.3%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • Toolathlon-Verified

    DeepSeek V4 Pro 081374.1%
    Source
    Ornith-1.5-35B-A3B48.7%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • Agents' Last Exam

    DeepSeek V4 Pro 081325.7%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • AutomationBench

    DeepSeek V4 Pro 081331.8%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    DeepSeek V4 Pro 081354.7%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • WideResearch

    DeepSeek V4 Pro 0813—
    Ornith-1.5-35B-A3B67.8%
    Source

    Not directly comparable

  • Claw-Eval

    DeepSeek V4 Pro 0813—
    Ornith-1.5-35B-A3B72.5%
    Source

    Not directly comparable

Coding

  • LiveCodeBench Pass@1-COT

    DeepSeek V4 Pro 081393.5%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • Codeforces

    DeepSeek V4 Pro 08133206.0
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • SWE-bench Verified

    DeepSeek V4 Pro 081380.6%
    Source
    Ornith-1.5-35B-A3B79%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • SWE-bench Pro

    DeepSeek V4 Pro 081355.4%
    Source
    Ornith-1.5-35B-A3B59.6%
    Source

    Ornith-1.5-35B-A3B leads this result

  • SWE Multilingual

    DeepSeek V4 Pro 081376.2%
    Source
    Ornith-1.5-35B-A3B71.4%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • Terminal-Bench 2.0

    DeepSeek V4 Pro 081367.9%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • Vibe Code Bench

    DeepSeek V4 Pro 081349.93%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek V4 Pro 081387.9%
    Source
    Ornith-1.5-35B-A3B67.8%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • NL2Repo

    DeepSeek V4 Pro 081361.5%
    Source
    Ornith-1.5-35B-A3B46.2%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • DeepSWE

    DeepSeek V4 Pro 081362.7%
    Source
    Ornith-1.5-35B-A3B22.0%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • DSBench-FullStack

    DeepSeek V4 Pro 081371.1%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • DSBench-Hard

    DeepSeek V4 Pro 081367.2%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • OpenHarmony Bench

    DeepSeek V4 Pro 081359.0%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • LiveCodeBench (Vals)

    DeepSeek V4 Pro 081387.5%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • SWE-bench (Vals)

    DeepSeek V4 Pro 081396.4%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • frontierBench

    DeepSeek V4 Pro 0813—
    Ornith-1.5-35B-A3B5.1%
    Source

    Not directly comparable

Reasoning

  • MRCR 1M

    DeepSeek V4 Pro 081383.5%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • CorpusQA 1M

    DeepSeek V4 Pro 081362.0%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • ARC-AGI-1

    DeepSeek V4 Pro 081390.00%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • ARC-AGI-2

    DeepSeek V4 Pro 081361.3%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

Knowledge

  • MMLU-Pro

    DeepSeek V4 Pro 081387.5%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • SimpleQA

    DeepSeek V4 Pro 081357.9%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • Chinese-SimpleQA

    DeepSeek V4 Pro 081384.4%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • GPQA

    DeepSeek V4 Pro 081390.1%
    Source
    Ornith-1.5-35B-A3B89.2%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • GPQA-D

    DeepSeek V4 Pro 081390.1%
    Source
    Ornith-1.5-35B-A3B89.2%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • HLE

    DeepSeek V4 Pro 081342.7%
    Source
    Ornith-1.5-35B-A3B25.6%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • GPQA Diamond (Vals)

    DeepSeek V4 Pro 081392.4%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • MMLU-Pro (Vals)

    DeepSeek V4 Pro 081387.0%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • HLE w/o tools

    DeepSeek V4 Pro 0813—
    Ornith-1.5-35B-A3B25.6%
    Source

    Not directly comparable

Math

  • HMMT Feb 2026

    DeepSeek V4 Pro 081395.2%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • IMOAnswerBench

    DeepSeek V4 Pro 081389.8%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • Apex

    DeepSeek V4 Pro 081338.3%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

  • Apex Shortlist

    DeepSeek V4 Pro 081390.2%
    Source
    Ornith-1.5-35B-A3B—

    Not directly comparable

46 public results · 14 shared

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Last updated October 2, 2026