Mercury 2.5 vs Qwen3.6 Plus

Updated October 23, 2026. Rank says Qwen3.6 Plus is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Qwen3.6 Plus has the higher public point estimate, 53.42 versus 33.38. Their conditional score ranges overlap. These ranges do not establish rank confidence. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Mercury 2.5

Inception

33.38/100

Estimated · Public rank #166

Conditional range 19.0–47.7

Model B

Qwen3.6 Plus

Alibaba

53.42/100

Supported · Public rank #78

90% interval 43.8–63.0

Shared results: 3
Mercury 2.5 only: 3
Qwen3.6 Plus only: 46
Like-for-like categories: 3 / 8

Estimated: Mercury 2.5 · Supported: Qwen3.6 Plus. 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.

Agentic work
Tool use, computer use, and multi-step task completion
Qwen3.6 Plus
Qwen3.6 Plus has the higher public agentic point estimate, 34.4 to 12, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Qwen3.6 Plus
Qwen3.6 Plus 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
Mercury 2.5 is scored on Estimated evidence for coding, 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.

Pick the job you would use them for

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

16.1Mercury 2.540.0Qwen3.6 Plus

Directional only · BenchAlign v5.8

Qwen3.6 Plus 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 leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

IFBenchInstruction following
Normalized gap 1.2
Mercury 2.5:77%
Qwen3.6 Plus:75.8%
Mercury 2.5 source Qwen3.6 Plus source

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

Like-for-like

Mercury 2.5: 12.0; Supported · #123/132
Qwen3.6 Plus: 34.4; Supported · #86/132
Basis: BenchAlign v5.8 lane · 3 vs 13 public rows
Reading: Qwen3.6 Plus leads

Knowledge

Like-for-like

Mercury 2.5: 33.8; Supported · #139/187
Qwen3.6 Plus: 52.8; Supported · #68/187
Basis: BenchAlign v5.8 lane · 1 vs 8 public rows
Reading: Qwen3.6 Plus leads · intervals overlap

Instruction following

Like-for-like

Mercury 2.5: 81.1; #60/153
Qwen3.6 Plus: 85.8; #49/153
Basis: Provisional lane · 1 vs 1 weighted rows
Reading: Qwen3.6 Plus leads

Coding

Directional only

Mercury 2.5: 16.1; Estimated · #133/153
Qwen3.6 Plus: 40.0; Supported · #68/153
Basis: BenchAlign v5.8 lane · 1 vs 9 public rows
Reading: Directional only

Reasoning

Not comparable

Mercury 2.5: 71.7; Unranked · 2 rankable rows
Qwen3.6 Plus: 73.9; Unranked · 4 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Mercury 2.5: Not ranked
Qwen3.6 Plus: 75.9; #22/56
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Mercury 2.5: Not ranked
Qwen3.6 Plus: 93.8; #4/17
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

Mercury 2.5: Not ranked
Qwen3.6 Plus: 69.5; #6/12
Basis: Provisional lane · 0 vs 3 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Mercury 2.5 and Qwen3.6 Plus
Category	Mercury 2.5	Qwen3.6 Plus	Basis	Reading
Agentic	12.0Supported · #123/132	34.4Supported · #86/132	Like-for-likeBenchAlign v5.8 lane · 3 vs 13 public rows	Qwen3.6 Plus leads
Knowledge	33.8Supported · #139/187	52.8Supported · #68/187	Like-for-likeBenchAlign v5.8 lane · 1 vs 8 public rows	Qwen3.6 Plus leads · intervals overlap
Instruction following	81.1#60/153	85.8#49/153	Like-for-likeProvisional lane · 1 vs 1 weighted rows	Qwen3.6 Plus leads
Coding	16.1Estimated · #133/153	40.0Supported · #68/153	Directional onlyBenchAlign v5.8 lane · 1 vs 9 public rows	Directional only
Reasoning	71.7Unranked · 2 rankable rows	73.9Unranked · 4 rankable rows	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multimodal	Not ranked	75.9#22/56	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multilingual	Not ranked	93.8#4/17	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Math	Not ranked	69.5#6/12	Not comparableProvisional lane · 0 vs 3 weighted rows	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

Mercury 2.5: $0.00012; Fits in one request
Qwen3.6 Plus: API rate not published; Fits in one request

Qwen3.6 Plus has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Mercury 2.5: $0.00245; Fits in one request
Qwen3.6 Plus: API rate not published; Fits in one request

Qwen3.6 Plus has no comparable published API token rate.

Cache-heavy agent loop

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

Mercury 2.5: $0.0031; Fits in one request
Qwen3.6 Plus: API rate not published; Fits in one request; Cached-input rate unavailable

Qwen3.6 Plus 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.

SpecificationMercury 2.5Qwen3.6 Plus

Context window

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

Mercury 2.5

260K

Inception models and Mercury 2.5 launch pricing

Qwen3.6 Plus

API model ID

Mercury 2.5

mercury-2.5

Inception models and Mercury 2.5 launch pricing

Qwen3.6 Plus

Not sourced

Cached-input rate

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

Mercury 2.5

$0.004 per 1M cached input tokens

Inception models and Mercury 2.5 launch pricing

Qwen3.6 Plus

No comparable hosted API rate

Documented inputs

Mercury 2.5

Not sourced

Qwen3.6 Plus

Not sourced

Documented outputs

Mercury 2.5

Not sourced

Qwen3.6 Plus

Not sourced

Provider availability

Mercury 2.5

Not sourced

Qwen3.6 Plus

Not sourced

Reasoning profile

Mercury 2.5

Reasoning

Qwen3.6 Plus

Reasoning

Weight access

Mercury 2.5

Proprietary

Qwen3.6 Plus

Proprietary

License

Mercury 2.5

Proprietary

Qwen3.6 Plus

Proprietary

Release date

Mercury 2.5

2026-09-08

Qwen3.6 Plus

2026-04-02

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: Qwen3.6 Plus has the higher public point estimate, 53.42 versus 33.38. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Qwen3.6 Plus has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Mercury 2.5 or Qwen3.6 Plus?

Qwen3.6 Plus has the higher public point estimate, 53.42 versus 33.38. Their conditional score ranges 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, Mercury 2.5 or Qwen3.6 Plus?

Qwen3.6 Plus scores higher for coding on the public lane, 40 to 16.1. Mercury 2.5 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, Mercury 2.5 or Qwen3.6 Plus?

Qwen3.6 Plus has the higher public agentic tasks point estimate, 34.4 to 12, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which costs less, Mercury 2.5 or Qwen3.6 Plus?

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, Mercury 2.5 or Qwen3.6 Plus?

Qwen3.6 Plus has the larger documented context window: 1M, compared with 260K.

Benchmark evidence

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

Browse raw public benchmark evidence52 rows

Agentic

τ³-bench results
Mercury 2.596.0%
Source
Qwen3.6 Plus70.7%
Source
Mercury 2.5 leads this result
DeepSearchQA
Mercury 2.534.0%
Source
Qwen3.6 Plus—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Mercury 2.534.5%
Source
Qwen3.6 Plus53.2%
Source
Qwen3.6 Plus leads this result
Terminal-Bench 2.0
Mercury 2.5—
Qwen3.6 Plus61.6%
Source
Not directly comparable
Claw-Eval
Mercury 2.5—
Qwen3.6 Plus58.8%
Source
Not directly comparable
QwenClawBench
Mercury 2.5—
Qwen3.6 Plus57.2%
Source
Not directly comparable
VITA-Bench
Mercury 2.5—
Qwen3.6 Plus44.3%
Source
Not directly comparable
DeepPlanning
Mercury 2.5—
Qwen3.6 Plus41.5%
Source
Not directly comparable
Toolathlon
Mercury 2.5—
Qwen3.6 Plus39.8%
Source
Not directly comparable
MCP Atlas
Mercury 2.5—
Qwen3.6 Plus48.2%
Source
Not directly comparable
MCP-Tasks
Mercury 2.5—
Qwen3.6 Plus74.1%
Source
Not directly comparable
WideResearch
Mercury 2.5—
Qwen3.6 Plus74.3%
Source
Not directly comparable
Gert Labs
Mercury 2.5—
Qwen3.6 Plus50.60%
Source
Not directly comparable
ResearchClawBench
Mercury 2.5—
Qwen3.6 Plus18.0%
Source
Not directly comparable

Coding

SciCode
Mercury 2.538%
Source
Qwen3.6 Plus—
Not directly comparable
SWE-bench Verified
Mercury 2.5—
Qwen3.6 Plus78.8%
Source
Not directly comparable
SWE-bench Pro
Mercury 2.5—
Qwen3.6 Plus56.6%
Source
Not directly comparable
SWE Multilingual
Mercury 2.5—
Qwen3.6 Plus73.8%
Source
Not directly comparable
LiveCodeBench v6
Mercury 2.5—
Qwen3.6 Plus87.1%
Source
Not directly comparable
NL2Repo
Mercury 2.5—
Qwen3.6 Plus37.9%
Source
Not directly comparable
Terminal-Bench 2.0
Mercury 2.5—
Qwen3.6 Plus61.6%
Source
Not directly comparable
Vibe Code Bench
Mercury 2.5—
Qwen3.6 Plus25.56%
Source
Not directly comparable
LiveCodeBench (Vals)
Mercury 2.5—
Qwen3.6 Plus86.0%
Source
Not directly comparable
SWE-bench (Vals)
Mercury 2.5—
Qwen3.6 Plus73.4%
Source
Not directly comparable

Reasoning

AI-Needle
Mercury 2.5—
Qwen3.6 Plus68.3%
Source
Not directly comparable
LongBench v2
Mercury 2.5—
Qwen3.6 Plus62%
Source
Not directly comparable

Multimodal

MMMU
Mercury 2.5—
Qwen3.6 Plus86.0%
Source
Not directly comparable
MMMU-Pro
Mercury 2.5—
Qwen3.6 Plus78.8%
Source
Not directly comparable
MathVision
Mercury 2.5—
Qwen3.6 Plus88.0%
Source
Not directly comparable
VideoMMMU
Mercury 2.5—
Qwen3.6 Plus84.0%
Source
Not directly comparable
ScreenSpot Pro
Mercury 2.5—
Qwen3.6 Plus68.2%
Source
Not directly comparable
CharXiv
Mercury 2.5—
Qwen3.6 Plus81.5%
Source
Not directly comparable
V*
Mercury 2.5—
Qwen3.6 Plus96.9%
Source
Not directly comparable

Knowledge

GPQA-D
Mercury 2.579.0%
Source
Qwen3.6 Plus—
Not directly comparable
GPQA
Mercury 2.5—
Qwen3.6 Plus90.4%
Source
Not directly comparable
SuperGPQA
Mercury 2.5—
Qwen3.6 Plus71.6%
Source
Not directly comparable
MMLU-Pro
Mercury 2.5—
Qwen3.6 Plus88.5%
Source
Not directly comparable
MMLU-Redux
Mercury 2.5—
Qwen3.6 Plus94.5%
Source
Not directly comparable
C-Eval
Mercury 2.5—
Qwen3.6 Plus93.3%
Source
Not directly comparable
HLE
Mercury 2.5—
Qwen3.6 Plus28.8%
Source
Not directly comparable
GPQA Diamond (Vals)
Mercury 2.5—
Qwen3.6 Plus87.4%
Source
Not directly comparable
MMLU-Pro (Vals)
Mercury 2.5—
Qwen3.6 Plus87.7%
Source
Not directly comparable

Multilingual

MMLU-ProX
Mercury 2.5—
Qwen3.6 Plus84.7%
Source
Not directly comparable
NOVA-63
Mercury 2.5—
Qwen3.6 Plus57.9%
Source
Not directly comparable

Instruction following

IFBench
Mercury 2.577%
Source
Qwen3.6 Plus75.8%
Source
Mercury 2.5 leads this result
IFEval
Mercury 2.5—
Qwen3.6 Plus94.3%
Source
Not directly comparable

Math

AIME26
Mercury 2.5—
Qwen3.6 Plus95.3%
Source
Not directly comparable
HMMT Feb 2025
Mercury 2.5—
Qwen3.6 Plus96.7%
Source
Not directly comparable
HMMT Nov 2025
Mercury 2.5—
Qwen3.6 Plus94.6%
Source
Not directly comparable
HMMT Feb 2026
Mercury 2.5—
Qwen3.6 Plus87.8%
Source
Not directly comparable
MMAnswerBench
Mercury 2.5—
Qwen3.6 Plus83.8%
Source
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Mercury 2.5—
Qwen3.6 Plus38.246%
Source
Not directly comparable

52 public results · 3 shared

Related comparisons

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Last updated October 23, 2026

Mercury 2.5 vs Qwen3.6 Plus

Decision reading

Which one for your work

Agentic work

Long documents

Coding work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Knowledge

Instruction following

Coding

Reasoning

Multimodal

Multilingual

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, Mercury 2.5 or Qwen3.6 Plus?

Which is better for coding, Mercury 2.5 or Qwen3.6 Plus?

Which is better for agentic tasks, Mercury 2.5 or Qwen3.6 Plus?

Which costs less, Mercury 2.5 or Qwen3.6 Plus?

Which has the larger context window, Mercury 2.5 or Qwen3.6 Plus?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch Mercury 2.5 vs Qwen3.6 Plus