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Command R7B (12-2024) vs Ling 3.0 Flash VL

Command R7B (12-2024) costs 1.2x less per output token than Ling 3.0 Flash VL ($0.15 vs $0.18 per million). Updated October 22, 2026; no benchmark has tested both models yet.

Command R7B (12-2024)Cohere
Coding
No data
Overall
No data
Output $/M
$0.15
Ling 3.0 Flash VLinclusionAI
Coding
No data
Overall
No data
Output $/M
$0.18
vs $0.15 vs $0.18 per 1M output tokens. Command R7B (12-2024) is 1.2x cheaper.

Command R7B (12-2024) or Ling 3.0 Flash VL: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick Command R7B (12-2024) for output tokens 1.2x cheaper ($0.15 vs $0.18 per million).

No benchmark has tested both yet. Command R7B (12-2024) has results on Berkeley Function Calling Leaderboard (BFCL); Ling 3.0 Flash VL on AA Coding Index, SciCode and AA Intelligence Index. Compare them on price and context instead.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
Command R7B (12-2024) not benchmarked, Ling 3.0 Flash VL not benchmarked.
Widest category
None yet: no category has been tested on both models.
Widest single gap
None yet: no benchmark has tested both models.
Output price
Command R7B (12-2024) $0.15 against Ling 3.0 Flash VL $0.18 per million output tokens, 1.2x apart.
Context window
Command R7B (12-2024) takes 128K tokens, Ling 3.0 Flash VL 131K.

Benchmark by benchmark

Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.

CodingDirectional onlySix of these boards feed the Vibe Coding Index2 boards measured
Coding benchmarks for Command R7B (12-2024) and Ling 3.0 Flash VL
BenchmarkCommand R7B (12-2024)Ling 3.0 Flash VLLeader
AA Coding IndexNot measured57.0Only Ling 3.0 Flash VL measured
SciCodeNot measured44.2%Only Ling 3.0 Flash VL measured
Neither model has a result on the other 41 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
Intelligence benchmarks for Command R7B (12-2024) and Ling 3.0 Flash VL
BenchmarkCommand R7B (12-2024)Ling 3.0 Flash VLLeader
AA Intelligence IndexNot measured24.6Only Ling 3.0 Flash VL measured
Neither model has a result on the other 1 board here
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
Agentic work benchmarks for Command R7B (12-2024) and Ling 3.0 Flash VL
BenchmarkCommand R7B (12-2024)Ling 3.0 Flash VLLeader
AA Agentic IndexNot measured28.7Only Ling 3.0 Flash VL measured
Berkeley Function Calling Leaderboard (BFCL)32.1%Not measuredOnly Command R7B (12-2024) measured
Neither model has a result on the other 40 boards here
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
Reasoning benchmarks for Command R7B (12-2024) and Ling 3.0 Flash VL
BenchmarkCommand R7B (12-2024)Ling 3.0 Flash VLLeader
GPQA Diamond (Artificial Analysis run)Not measured86.2%Only Ling 3.0 Flash VL measured
Humanity's Last Exam (Artificial Analysis run)Not measured22.0%Only Ling 3.0 Flash VL measured
Neither model has a result on the other 21 boards here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
General benchmarks for Command R7B (12-2024) and Ling 3.0 Flash VL
BenchmarkCommand R7B (12-2024)Ling 3.0 Flash VLLeader
Long context reasoning (AA-LCR)Not measured78.3%Only Ling 3.0 Flash VL measured
Neither model has a result on the other 47 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

Command R7B (12-2024)$0.0001Fits in one request
Ling 3.0 Flash VL$0.0001Fits in one request

Command R7B (12-2024) costs less

Repository review

50K input + 3K output tokens

Command R7B (12-2024)$0.0023Fits in one request
Ling 3.0 Flash VL$0.0035Fits in one request

Command R7B (12-2024) costs less

Agent loop

200K input + 10K output tokens

Command R7B (12-2024)$0.0090Exceeds the context window
Ling 3.0 Flash VL$0.014Exceeds the context window

Command R7B (12-2024) costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of Command R7B (12-2024) and Ling 3.0 Flash VL
SpecificationCohere Command R7B (12-2024)inclusionAI Ling 3.0 Flash VL
MakerCohereinclusionAI
WeightsNo public weights listedOpen weights
ReleasedDecember 14, 2024September 10, 2026
Context window128K131K
Input $/Mtok$0.0375$0.06
Output $/Mtok$0.15$0.18
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Command R7B (12-2024) better than Ling 3.0 Flash VL for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Command R7B (12-2024) for output tokens 1.2x cheaper ($0.15 vs $0.18 per million).

Which is cheaper, Command R7B (12-2024) or Ling 3.0 Flash VL?

Command R7B (12-2024) costs $0.0375 per million input tokens and $0.15 per million output tokens; Ling 3.0 Flash VL costs $0.06 and $0.18. Cost per solved task is known for neither of them.

Which has the bigger context window, Command R7B (12-2024) or Ling 3.0 Flash VL?

Ling 3.0 Flash VL, with 131K tokens against 128K.

Has any benchmark tested both Command R7B (12-2024) and Ling 3.0 Flash VL?

No benchmark has tested both yet. Command R7B (12-2024) has results on Berkeley Function Calling Leaderboard (BFCL); Ling 3.0 Flash VL on AA Coding Index, SciCode and AA Intelligence Index. Compare them on price and context instead. Data as of October 22, 2026.

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