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Qwen2.5 VL 72B Instruct vs Qwen3.6 27B

Qwen2.5 VL 72B Instruct costs 2.7x less per output token than Qwen3.6 27B ($1.00 vs $2.70 per million). Updated October 22, 2026; no benchmark has tested both models yet.

Replace Qwen2.5 VL 72B Instruct Replace Qwen3.6 27B
Qwen2.5 VL 72B InstructQwen
Coding
No data
Overall
No data
Output $/M
$1.00
Qwen3.6 27BQwen
Coding
36.8
Overall
45.3
Output $/M
$2.70
vs $1.00 vs $2.70 per 1M output tokens. Qwen2.5 VL 72B Instruct is 2.7x cheaper.

Qwen2.5 VL 72B Instruct or Qwen3.6 27B: which to pick

Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; Qwen2.5 VL 72B Instruct has none, so the two cannot be ranked on coding.

Pick Qwen2.5 VL 72B Instruct for output tokens 2.7x cheaper ($1.00 vs $2.70 per million).

Model AQwen2.5 VL 72B InstructQwenNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.6 27BQwen36.8/1002 independent benchmarks$1.30 per solved task

No benchmark has tested both yet. Qwen2.5 VL 72B Instruct has results on OSWorld, Kagi LLM Benchmark and GeoBench; Qwen3.6 27B on AA Coding Index, SWE-bench Verified (Vals AI) and Vibe Code Bench (Vals AI). 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
Qwen2.5 VL 72B Instruct not benchmarked, Qwen3.6 27B 36.8 from 2 benchmarks.
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
Qwen2.5 VL 72B Instruct $1.00 against Qwen3.6 27B $2.70 per million output tokens, 2.7x apart.
Context window
Qwen2.5 VL 72B Instruct takes 128K tokens, Qwen3.6 27B 262K.

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 Index5 boards measured
Coding benchmarks for Qwen2.5 VL 72B Instruct and Qwen3.6 27B
BenchmarkQwen2.5 VL 72B InstructQwen3.6 27BLeader
AA Coding IndexNot measured53.7Only Qwen3.6 27B measured
SWE-bench Verified (Vals AI)Not measured70.0%Only Qwen3.6 27B measured
Vibe Code Bench (Vals AI)Not measured11.9%Only Qwen3.6 27B measured
SciCodeNot measured42.8%Only Qwen3.6 27B measured
Terminal-Bench Hard (Artificial Analysis run)Not measured34.8%Only Qwen3.6 27B measured
Neither model has a result on the other 38 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
Intelligence benchmarks for Qwen2.5 VL 72B Instruct and Qwen3.6 27B
BenchmarkQwen2.5 VL 72B InstructQwen3.6 27BLeader
AA Intelligence IndexNot measured21.4Only Qwen3.6 27B measured
Epoch Capabilities IndexNot measured146.2Only Qwen3.6 27B measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
Agentic work benchmarks for Qwen2.5 VL 72B Instruct and Qwen3.6 27B
BenchmarkQwen2.5 VL 72B InstructQwen3.6 27BLeader
AA Agentic IndexNot measured18.5Only Qwen3.6 27B measured
τ²-Bench TelecomNot measured94.2%Only Qwen3.6 27B measured
OSWorld5.0%Not measuredOnly Qwen2.5 VL 72B Instruct measured
Neither model has a result on the other 39 boards here
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
Reasoning benchmarks for Qwen2.5 VL 72B Instruct and Qwen3.6 27B
BenchmarkQwen2.5 VL 72B InstructQwen3.6 27BLeader
GPQA Diamond (Artificial Analysis run)Not measured84.2%Only Qwen3.6 27B measured
Humanity's Last Exam (Artificial Analysis run)Not measured23.1%Only Qwen3.6 27B measured
GPQA Diamond (Epoch AI run)Not measured85.9%Only Qwen3.6 27B measured
Mystery game puzzlesNot measured7.0%Only Qwen3.6 27B measured
Kagi LLM Benchmark36.0%Not measuredOnly Qwen2.5 VL 72B Instruct measured
Neither model has a result on the other 18 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
Math benchmarks for Qwen2.5 VL 72B Instruct and Qwen3.6 27B
BenchmarkQwen2.5 VL 72B InstructQwen3.6 27BLeader
OTIS Mock AIMENot measured91.1%Only Qwen3.6 27B measured
FrontierMath, tiers 1 to 3Not measured35.1%Only Qwen3.6 27B measured
Neither model has a result on the other 12 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
Knowledge benchmarks for Qwen2.5 VL 72B Instruct and Qwen3.6 27B
BenchmarkQwen2.5 VL 72B InstructQwen3.6 27BLeader
CaseLaw (Vals AI)Not measured53.2%Only Qwen3.6 27B measured
CorpFin (Vals AI)Not measured62.3%Only Qwen3.6 27B measured
TaxEval (Vals AI)Not measured71.3%Only Qwen3.6 27B measured
MortgageTax (Vals AI)Not measured68.3%Only Qwen3.6 27B measured
GeoBench62.0%Not measuredOnly Qwen2.5 VL 72B Instruct measured
Neither model has a result on the other 15 boards here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index9 boards measured
General benchmarks for Qwen2.5 VL 72B Instruct and Qwen3.6 27B
BenchmarkQwen2.5 VL 72B InstructQwen3.6 27BLeader
SAGE (Vals AI)Not measured45.6%Only Qwen3.6 27B measured
IFBenchNot measured67.6%Only Qwen3.6 27B measured
Long context reasoning (AA-LCR)Not measured77.3%Only Qwen3.6 27B measured
DTBenchNot measured78.1%Only Qwen3.6 27B measured
LMCANot measured34.5%Only Qwen3.6 27B measured
LMArena Vision1107Not measuredOnly Qwen2.5 VL 72B Instruct measured
LMArena Vision: English1115Not measuredOnly Qwen2.5 VL 72B Instruct measured
LMArena Vision: Chinese1107Not measuredOnly Qwen2.5 VL 72B Instruct measured
EQ-Bench 4Not measured1026Only Qwen3.6 27B measured
Neither model has a result on the other 39 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

Qwen2.5 VL 72B Instruct$0.0013Fits in one request
Qwen3.6 27B$0.0018Fits in one request

Qwen2.5 VL 72B Instruct costs less

Repository review

50K input + 3K output tokens

Qwen2.5 VL 72B Instruct$0.043Fits in one request
Qwen3.6 27B$0.031Fits in one request

Qwen3.6 27B costs less

Agent loop

200K input + 10K output tokens

Qwen2.5 VL 72B Instruct$0.170Exceeds the context window
Qwen3.6 27B$0.117Fits in one request

Qwen3.6 27B 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 Qwen2.5 VL 72B Instruct and Qwen3.6 27B
SpecificationQwen2.5 VL 72B InstructQwen3.6 27B
MakerQwenQwen
WeightsOpen weightsOpen weights
ReleasedFebruary 1, 2025April 27, 2026
Context window128K262K
Input $/Mtok$0.80$0.45
Output $/Mtok$1.00$2.70
Coding indexNot benchmarked36.8
Independent measurements behind it02
Cost per solved taskNot computable$1.30
SourceOpenRouter listingOpenRouter listing

Questions

Is Qwen2.5 VL 72B Instruct better than Qwen3.6 27B for coding?

Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; Qwen2.5 VL 72B Instruct has none, so the two cannot be ranked on coding. Pick Qwen2.5 VL 72B Instruct for output tokens 2.7x cheaper ($1.00 vs $2.70 per million).

Which is cheaper, Qwen2.5 VL 72B Instruct or Qwen3.6 27B?

Qwen2.5 VL 72B Instruct costs $0.80 per million input tokens and $1.00 per million output tokens; Qwen3.6 27B costs $0.45 and $2.70. Cost per solved task is known for Qwen3.6 27B only.

Which has the bigger context window, Qwen2.5 VL 72B Instruct or Qwen3.6 27B?

Qwen3.6 27B, with 262K tokens against 128K.

Has any benchmark tested both Qwen2.5 VL 72B Instruct and Qwen3.6 27B?

No benchmark has tested both yet. Qwen2.5 VL 72B Instruct has results on OSWorld, Kagi LLM Benchmark and GeoBench; Qwen3.6 27B on AA Coding Index, SWE-bench Verified (Vals AI) and Vibe Code Bench (Vals AI). Compare them on price and context instead. Data as of October 22, 2026.

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