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BenchLM
Data

Claude Haiku 4.5 vs Qwen3.6 Plus

Updated October 17, 2026. Rank says Qwen3.6 Plus is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Qwen3.6 Plus has the higher public point estimate, 53.78 versus 41.16. Their conditional score ranges overlap. These ranges do not establish rank confidence. 6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Anthropic logo

Anthropic

41.16/100

Estimated · Public rank #129

Conditional range 31.4–50.9

Model B
Alibaba logo

Alibaba

53.78/100

Supported · Public rank #75

90% interval 44.2–63.4

Shared results
6
Claude Haiku 4.5 only
3
Qwen3.6 Plus only
43
Like-for-like categories
2 / 8
Estimated: Claude Haiku 4.5 · 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.

  • Coding work

    Code generation, repair, and software-engineering tasks

    Qwen3.6 Plus

    Qwen3.6 Plus has the higher public coding point estimate, 40 to 16.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

    Confidence: limited
  • 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 21.4, 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
  • 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

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Claude Haiku 4.5 does not fit this workload in one request. Qwen3.6 Plus has no comparable published API token rate.

    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.

16.8Claude Haiku 4.540.0Qwen3.6 Plus

Like-for-like · 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.

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.

1 category rests 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

Like-for-like
Claude Haiku 4.5
21.4
Supported · #107/128
Qwen3.6 Plus
34.4
Supported · #83/128
Basis
BenchAlign v5.8 lane · 3 vs 13 public rows
Reading
Qwen3.6 Plus leads · intervals overlap

Coding

Like-for-like
Claude Haiku 4.5
16.8
Supported · #126/150
Qwen3.6 Plus
40.0
Supported · #66/150
Basis
BenchAlign v5.8 lane · 4 vs 9 public rows
Reading
Qwen3.6 Plus leads

Knowledge

Directional only
Claude Haiku 4.5
33.3
Estimated · #141/184
Qwen3.6 Plus
53.4
Supported · #66/184
Basis
BenchAlign v5.8 lane · 2 vs 8 public rows
Reading
Directional only

Reasoning

Not comparable
Claude Haiku 4.5
Not ranked
Qwen3.6 Plus
74.7
Unranked · 4 rankable rows
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude Haiku 4.5
Not ranked
Qwen3.6 Plus
76.1
#21/54
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Haiku 4.5
Not ranked
Qwen3.6 Plus
93.8
#4/17
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Haiku 4.5
Not ranked
Qwen3.6 Plus
85.8
#47/148
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Haiku 4.5
Not ranked
Qwen3.6 Plus
69.5
#6/12
Basis
Provisional lane · 0 vs 3 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

Claude Haiku 4.5
$0.0035
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

Claude Haiku 4.5
$0.065
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

Claude Haiku 4.5
$0.09
Does not fit in one request
Qwen3.6 Plus
API rate not published
Fits in one request
Cached-input rate unavailable

Claude Haiku 4.5 does not fit this workload in one request. 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.

Context window

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

Claude Haiku 4.5

Qwen3.6 Plus

1M

API model ID

Claude Haiku 4.5

claude-haiku-4-5-20251001

Claude API 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.

Claude Haiku 4.5

$0.1 per 1M cached input tokens

Claude API pricing

Qwen3.6 Plus

No comparable hosted API rate

Documented inputs

Claude Haiku 4.5

Not sourced

Qwen3.6 Plus

Not sourced

Documented outputs

Claude Haiku 4.5

Not sourced

Qwen3.6 Plus

Not sourced

Provider availability

Claude Haiku 4.5

Not sourced

Qwen3.6 Plus

Not sourced

Reasoning profile

Claude Haiku 4.5

Non-Reasoning

Qwen3.6 Plus

Reasoning

Weight access

Claude Haiku 4.5

Proprietary

Qwen3.6 Plus

Proprietary

License

Claude Haiku 4.5

Proprietary

Qwen3.6 Plus

Proprietary

Release date

Claude Haiku 4.5

2025-10-15

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, 53.78 versus 41.16. 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, Claude Haiku 4.5 or Qwen3.6 Plus?

Qwen3.6 Plus has the higher public point estimate, 53.78 versus 41.16. 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, Claude Haiku 4.5 or Qwen3.6 Plus?

Qwen3.6 Plus has the higher public coding point estimate, 40 to 16.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, Claude Haiku 4.5 or Qwen3.6 Plus?

Qwen3.6 Plus has the higher public agentic tasks point estimate, 34.4 to 21.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which costs less, Claude Haiku 4.5 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, Claude Haiku 4.5 or Qwen3.6 Plus?

Qwen3.6 Plus has the larger documented context window: 1M, compared with 200K.

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

  • JobBench

    Claude Haiku 4.516.0%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Claude Haiku 4.543.8%
    Source
    Qwen3.6 Plus53.2%
    Source

    Qwen3.6 Plus leads this result

  • MazeBench

    Claude Haiku 4.50%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Haiku 4.5—
    Qwen3.6 Plus61.6%
    Source

    Not directly comparable

  • Claw-Eval

    Claude Haiku 4.5—
    Qwen3.6 Plus58.8%
    Source

    Not directly comparable

  • QwenClawBench

    Claude Haiku 4.5—
    Qwen3.6 Plus57.2%
    Source

    Not directly comparable

  • τ³-bench results

    Claude Haiku 4.5—
    Qwen3.6 Plus70.7%
    Source

    Not directly comparable

  • VITA-Bench

    Claude Haiku 4.5—
    Qwen3.6 Plus44.3%
    Source

    Not directly comparable

  • DeepPlanning

    Claude Haiku 4.5—
    Qwen3.6 Plus41.5%
    Source

    Not directly comparable

  • Toolathlon

    Claude Haiku 4.5—
    Qwen3.6 Plus39.8%
    Source

    Not directly comparable

  • MCP Atlas

    Claude Haiku 4.5—
    Qwen3.6 Plus48.2%
    Source

    Not directly comparable

  • MCP-Tasks

    Claude Haiku 4.5—
    Qwen3.6 Plus74.1%
    Source

    Not directly comparable

  • WideResearch

    Claude Haiku 4.5—
    Qwen3.6 Plus74.3%
    Source

    Not directly comparable

  • Gert Labs

    Claude Haiku 4.5—
    Qwen3.6 Plus50.60%
    Source

    Not directly comparable

  • ResearchClawBench

    Claude Haiku 4.5—
    Qwen3.6 Plus18.0%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Haiku 4.573.3%
    Source
    Qwen3.6 Plus78.8%
    Source

    Qwen3.6 Plus leads this result

  • VulcanBench v3

    Claude Haiku 4.576.2%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • LiveCodeBench (Vals)

    Claude Haiku 4.541.2%
    Source
    Qwen3.6 Plus86.0%
    Source

    Qwen3.6 Plus leads this result

  • SWE-bench (Vals)

    Claude Haiku 4.566.6%
    Source
    Qwen3.6 Plus73.4%
    Source

    Qwen3.6 Plus leads this result

  • SWE-bench Pro

    Claude Haiku 4.5—
    Qwen3.6 Plus56.6%
    Source

    Not directly comparable

  • SWE Multilingual

    Claude Haiku 4.5—
    Qwen3.6 Plus73.8%
    Source

    Not directly comparable

  • LiveCodeBench v6

    Claude Haiku 4.5—
    Qwen3.6 Plus87.1%
    Source

    Not directly comparable

  • NL2Repo

    Claude Haiku 4.5—
    Qwen3.6 Plus37.9%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Haiku 4.5—
    Qwen3.6 Plus61.6%
    Source

    Not directly comparable

  • Vibe Code Bench

    Claude Haiku 4.5—
    Qwen3.6 Plus25.56%
    Source

    Not directly comparable

Reasoning

  • AI-Needle

    Claude Haiku 4.5—
    Qwen3.6 Plus68.3%
    Source

    Not directly comparable

  • LongBench v2

    Claude Haiku 4.5—
    Qwen3.6 Plus62%
    Source

    Not directly comparable

Multimodal

  • MMMU

    Claude Haiku 4.5—
    Qwen3.6 Plus86.0%
    Source

    Not directly comparable

  • MMMU-Pro

    Claude Haiku 4.5—
    Qwen3.6 Plus78.8%
    Source

    Not directly comparable

  • MathVision

    Claude Haiku 4.5—
    Qwen3.6 Plus88.0%
    Source

    Not directly comparable

  • VideoMMMU

    Claude Haiku 4.5—
    Qwen3.6 Plus84.0%
    Source

    Not directly comparable

  • ScreenSpot Pro

    Claude Haiku 4.5—
    Qwen3.6 Plus68.2%
    Source

    Not directly comparable

  • CharXiv

    Claude Haiku 4.5—
    Qwen3.6 Plus81.5%
    Source

    Not directly comparable

  • V*

    Claude Haiku 4.5—
    Qwen3.6 Plus96.9%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    Claude Haiku 4.572.2%
    Source
    Qwen3.6 Plus87.4%
    Source

    Qwen3.6 Plus leads this result

  • MMLU-Pro (Vals)

    Claude Haiku 4.578.7%
    Source
    Qwen3.6 Plus87.7%
    Source

    Qwen3.6 Plus leads this result

  • GPQA

    Claude Haiku 4.5—
    Qwen3.6 Plus90.4%
    Source

    Not directly comparable

  • SuperGPQA

    Claude Haiku 4.5—
    Qwen3.6 Plus71.6%
    Source

    Not directly comparable

  • MMLU-Pro

    Claude Haiku 4.5—
    Qwen3.6 Plus88.5%
    Source

    Not directly comparable

  • MMLU-Redux

    Claude Haiku 4.5—
    Qwen3.6 Plus94.5%
    Source

    Not directly comparable

  • C-Eval

    Claude Haiku 4.5—
    Qwen3.6 Plus93.3%
    Source

    Not directly comparable

  • HLE

    Claude Haiku 4.5—
    Qwen3.6 Plus28.8%
    Source

    Not directly comparable

Multilingual

  • MMLU-ProX

    Claude Haiku 4.5—
    Qwen3.6 Plus84.7%
    Source

    Not directly comparable

  • NOVA-63

    Claude Haiku 4.5—
    Qwen3.6 Plus57.9%
    Source

    Not directly comparable

Instruction following

  • IFEval

    Claude Haiku 4.5—
    Qwen3.6 Plus94.3%
    Source

    Not directly comparable

  • IFBench

    Claude Haiku 4.5—
    Qwen3.6 Plus75.8%
    Source

    Not directly comparable

Math

  • AIME26

    Claude Haiku 4.5—
    Qwen3.6 Plus95.3%
    Source

    Not directly comparable

  • HMMT Feb 2025

    Claude Haiku 4.5—
    Qwen3.6 Plus96.7%
    Source

    Not directly comparable

  • HMMT Nov 2025

    Claude Haiku 4.5—
    Qwen3.6 Plus94.6%
    Source

    Not directly comparable

  • HMMT Feb 2026

    Claude Haiku 4.5—
    Qwen3.6 Plus87.8%
    Source

    Not directly comparable

  • MMAnswerBench

    Claude Haiku 4.5—
    Qwen3.6 Plus83.8%
    Source

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Claude Haiku 4.5—
    Qwen3.6 Plus38.246%
    Source

    Not directly comparable

52 public results · 6 shared

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Last updated October 17, 2026