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BenchLM

Command A+ vs DeepSeek V4 Pro 0813

Updated September 27, 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

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Cohere logo
Command A+

Cohere

36.17/100

Estimated · Public rank #128

90% interval 24.7–47.7

Model B
DeepSeek logo
DeepSeek V4 Pro 0813

DeepSeek

63.48/100

Estimated · Public rank #33

90% interval 52.0–75.0

Shared results
0
Command A+ only
4
DeepSeek V4 Pro 0813 only
42
Like-for-like categories
0 / 8
Estimated: Command A+ and DeepSeek V4 Pro 0813How 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

  • Chat turn cost

    1K fresh input + 500 output tokens

    DeepSeek V4 Pro 0813

    DeepSeek V4 Pro 0813 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates
Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    DeepSeek V4 Pro 0813

    DeepSeek V4 Pro 0813 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Command A+ 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

    Command A+ is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Cache-heavy agent loop cost

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

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Command A+ does not fit this workload in one request. Command A+ has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

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.

26.3Command A+50.3DeepSeek V4 Pro 0813

Directional only · BenchAlign v5.7

DeepSeek V4 Pro 0813 scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 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
Command A+
19.2
Estimated · #89/105
DeepSeek V4 Pro 0813
55.0
Supported · #29/105
Basis
BenchAlign v5.7 lane · 1 vs 11 public rows
Reading
Directional only

Coding

Directional only
Command A+
26.3
Estimated · #98/135
DeepSeek V4 Pro 0813
50.3
Supported · #39/135
Basis
BenchAlign v5.7 lane · 0 vs 15 public rows
Reading
Directional only

Knowledge

Directional only
Command A+
29.5
Estimated · #133/158
DeepSeek V4 Pro 0813
63.4
Estimated · #29/158
Basis
BenchAlign v5.7 lane · 0 vs 8 public rows
Reading
Directional only

Reasoning

Not comparable
Command A+
58.3
Unranked · 2 rankable rows
DeepSeek V4 Pro 0813
56.9
Unranked · 4 rankable rows
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Command A+
15.1
#49/50
DeepSeek V4 Pro 0813
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Command A+
Not ranked
DeepSeek V4 Pro 0813
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Command A+
89.2
#21/124
DeepSeek V4 Pro 0813
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Command A+
Not ranked
DeepSeek V4 Pro 0813
80.2
Unranked · 4 rankable rows
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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

Command A+
$0.0075
Fits in one request
DeepSeek V4 Pro 0813
$0.0033
Fits in one request

DeepSeek V4 Pro 0813 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Command A+
$0.155
Fits in one request
DeepSeek V4 Pro 0813
$0.07788
Fits in one request

DeepSeek V4 Pro 0813 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Command A+
$0.65
Does not fit in one request
Cached input priced at the published list-input rate
DeepSeek V4 Pro 0813
$0.0748
Fits in one request

Command A+ does not fit this workload in one request. Command A+ has no published cached-input rate, so cached tokens use its listed input 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.

Context window

Maximum documented context; output-token limits may be lower.

Command A+

128K

DeepSeek V4 Pro 0813

1M

DeepSeek models and pricing

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Command A+

Not published

DeepSeek V4 Pro 0813

$0.044 per 1M cached input tokens

DeepSeek: Models & Pricing

Reasoning profile

Command A+

Reasoning

DeepSeek V4 Pro 0813

Reasoning

Weight access

Command A+

Open Weight

DeepSeek V4 Pro 0813

Open Weight

License

Command A+

Open Weight

DeepSeek V4 Pro 0813

Open Weight

Release date

Command A+

2026-05-20

DeepSeek V4 Pro 0813

2026-08-13

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
Repository review: $0.155 vs $0.07788. Cache-heavy agent loop: $0.65 vs $0.0748.
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, Command A+ or DeepSeek V4 Pro 0813?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Command A+ or DeepSeek V4 Pro 0813?

DeepSeek V4 Pro 0813 scores higher for coding on the public lane, 50.3 to 26.3. Command A+ 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, Command A+ or DeepSeek V4 Pro 0813?

DeepSeek V4 Pro 0813 scores higher for agentic tasks on the public lane, 55 to 19.2. Command A+ 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, Command A+ or DeepSeek V4 Pro 0813?

For the stated presets, chat costs $0.0075 on Command A+ and $0.0033 on DeepSeek V4 Pro 0813; repository review costs $0.155 and $0.07788; the cache-heavy agent loop costs $0.65 and $0.0748. Command A+ does not fit this workload in one request. Command A+ has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Command A+ or DeepSeek V4 Pro 0813?

DeepSeek V4 Pro 0813 has the larger documented context window: 1M, compared with 128K.

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

  • τ²-bench results

    Command A+85%
    Source
    DeepSeek V4 Pro 0813—

    Not directly comparable

  • Terminal-Bench 2.0

    Command A+—
    DeepSeek V4 Pro 081367.9%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Command A+—
    DeepSeek V4 Pro 081387.9%
    Source

    Not directly comparable

  • BrowseComp

    Command A+—
    DeepSeek V4 Pro 081383.4%
    Source

    Not directly comparable

  • HLE w/ tools

    Command A+—
    DeepSeek V4 Pro 081360.0%
    Source

    Not directly comparable

  • MCP Atlas

    Command A+—
    DeepSeek V4 Pro 081373.6%
    Source

    Not directly comparable

  • Toolathlon

    Command A+—
    DeepSeek V4 Pro 081351.8%
    Source

    Not directly comparable

  • CyberGym

    Command A+—
    DeepSeek V4 Pro 081383.3%
    Source

    Not directly comparable

  • Toolathlon-Verified

    Command A+—
    DeepSeek V4 Pro 081374.1%
    Source

    Not directly comparable

  • Agents' Last Exam

    Command A+—
    DeepSeek V4 Pro 081325.7%
    Source

    Not directly comparable

  • AutomationBench

    Command A+—
    DeepSeek V4 Pro 081331.8%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Command A+—
    DeepSeek V4 Pro 081354.7%
    Source

    Not directly comparable

Coding

  • LiveCodeBench Pass@1-COT

    Command A+—
    DeepSeek V4 Pro 081393.5%
    Source

    Not directly comparable

  • Codeforces

    Command A+—
    DeepSeek V4 Pro 08133206.0
    Source

    Not directly comparable

  • SWE-bench Verified

    Command A+—
    DeepSeek V4 Pro 081380.6%
    Source

    Not directly comparable

  • SWE-bench Pro

    Command A+—
    DeepSeek V4 Pro 081355.4%
    Source

    Not directly comparable

  • SWE Multilingual

    Command A+—
    DeepSeek V4 Pro 081376.2%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Command A+—
    DeepSeek V4 Pro 081367.9%
    Source

    Not directly comparable

  • Vibe Code Bench

    Command A+—
    DeepSeek V4 Pro 081349.93%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Command A+—
    DeepSeek V4 Pro 081387.9%
    Source

    Not directly comparable

  • NL2Repo

    Command A+—
    DeepSeek V4 Pro 081361.5%
    Source

    Not directly comparable

  • DeepSWE

    Command A+—
    DeepSeek V4 Pro 081362.7%
    Source

    Not directly comparable

  • DSBench-FullStack

    Command A+—
    DeepSeek V4 Pro 081371.1%
    Source

    Not directly comparable

  • DSBench-Hard

    Command A+—
    DeepSeek V4 Pro 081367.2%
    Source

    Not directly comparable

  • OpenHarmony Bench

    Command A+—
    DeepSeek V4 Pro 081359.0%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    Command A+—
    DeepSeek V4 Pro 081387.5%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Command A+—
    DeepSeek V4 Pro 081396.4%
    Source

    Not directly comparable

Reasoning

  • MRCR 1M

    Command A+—
    DeepSeek V4 Pro 081383.5%
    Source

    Not directly comparable

  • CorpusQA 1M

    Command A+—
    DeepSeek V4 Pro 081362.0%
    Source

    Not directly comparable

  • ARC-AGI-1

    Command A+—
    DeepSeek V4 Pro 081390.00%
    Source

    Not directly comparable

  • ARC-AGI-2

    Command A+—
    DeepSeek V4 Pro 081361.3%
    Source

    Not directly comparable

Multimodal

  • MMMU

    Command A+75.1%
    Source
    DeepSeek V4 Pro 0813—

    Not directly comparable

  • MMMU-Pro

    Command A+63%
    Source
    DeepSeek V4 Pro 0813—

    Not directly comparable

  • CharXiv

    Command A+52.7%
    Source
    DeepSeek V4 Pro 0813—

    Not directly comparable

Knowledge

  • MMLU-Pro

    Command A+—
    DeepSeek V4 Pro 081387.5%
    Source

    Not directly comparable

  • SimpleQA

    Command A+—
    DeepSeek V4 Pro 081357.9%
    Source

    Not directly comparable

  • Chinese-SimpleQA

    Command A+—
    DeepSeek V4 Pro 081384.4%
    Source

    Not directly comparable

  • GPQA

    Command A+—
    DeepSeek V4 Pro 081390.1%
    Source

    Not directly comparable

  • GPQA-D

    Command A+—
    DeepSeek V4 Pro 081390.1%
    Source

    Not directly comparable

  • HLE

    Command A+—
    DeepSeek V4 Pro 081342.7%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    Command A+—
    DeepSeek V4 Pro 081392.4%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    Command A+—
    DeepSeek V4 Pro 081387.0%
    Source

    Not directly comparable

Math

  • HMMT Feb 2026

    Command A+—
    DeepSeek V4 Pro 081395.2%
    Source

    Not directly comparable

  • IMOAnswerBench

    Command A+—
    DeepSeek V4 Pro 081389.8%
    Source

    Not directly comparable

  • Apex

    Command A+—
    DeepSeek V4 Pro 081338.3%
    Source

    Not directly comparable

  • Apex Shortlist

    Command A+—
    DeepSeek V4 Pro 081390.2%
    Source

    Not directly comparable

46 public results · 0 shared

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Last updated September 27, 2026