Mistral Medium 3.5 128B vs Qwen3.6 Plus

Updated September 29, 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 score estimate, 54.44 versus 36.09, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 7 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Mistral Medium 3.5 128B

Mistral

36.09/100

Estimated · Public rank #137

90% interval 24.6–47.6

Model B

Qwen3.6 Plus

Alibaba

54.44/100

Supported · Public rank #66

90% interval 45.6–63.3

Shared results: 7
Mistral Medium 3.5 128B only: 0
Qwen3.6 Plus only: 41
Like-for-like categories: 2 / 8

Estimated: Mistral Medium 3.5 128B · Supported: Qwen3.6 PlusHow 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 leads on the public agentic lane, 33.4 to 19.7, with Supported evidence for both models, although the 90% intervals overlap.
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
Mistral Medium 3.5 128B 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: rate-fallback
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.

26.6Mistral Medium 3.5 128B42.5Qwen3.6 Plus

Directional only · BenchAlign v5.7

Qwen3.6 Plus 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 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.

MMLU-Pro (Vals)Knowledge
Normalized gap 12.4
Mistral Medium 3.5 128B:75.3%
Qwen3.6 Plus:87.7%
Mistral Medium 3.5 128B source Qwen3.6 Plus source
SWE-bench VerifiedCoding
Normalized gap 1.2
Mistral Medium 3.5 128B:77.6%
Qwen3.6 Plus:78.8%
Mistral Medium 3.5 128B 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 v5.7 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

Like-for-like

Mistral Medium 3.5 128B: 19.7; Supported · #100/117
Qwen3.6 Plus: 33.4; Supported · #72/117
Basis: BenchAlign v5.7 lane · 3 vs 13 public rows
Reading: Qwen3.6 Plus leads · intervals overlap

Knowledge

Like-for-like

Mistral Medium 3.5 128B: 33.6; Supported · #121/169
Qwen3.6 Plus: 52.1; Supported · #58/169
Basis: BenchAlign v5.7 lane · 2 vs 8 public rows
Reading: Qwen3.6 Plus leads · intervals overlap

Coding

Directional only

Mistral Medium 3.5 128B: 26.6; Estimated · #102/143
Qwen3.6 Plus: 42.5; Supported · #57/143
Basis: BenchAlign v5.7 lane · 2 vs 7 public rows
Reading: Directional only

Instruction following

Directional only

Mistral Medium 3.5 128B: 82.6; #48/124
Qwen3.6 Plus: 82.5; #50/124
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Directional only

Reasoning

Not comparable

Mistral Medium 3.5 128B: 69.9; Unranked · 2 rankable rows
Qwen3.6 Plus: 61.3; Unranked · 4 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Mistral Medium 3.5 128B: 56.7; Unranked · 1 rankable row
Qwen3.6 Plus: 67.2; #24/50
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Mistral Medium 3.5 128B: Not ranked
Qwen3.6 Plus: 69.7; #4/12
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

Mistral Medium 3.5 128B: Not ranked
Qwen3.6 Plus: 62.0; #5/7
Basis: Provisional lane · 0 vs 4 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Mistral Medium 3.5 128B and Qwen3.6 Plus
Category	Mistral Medium 3.5 128B	Qwen3.6 Plus	Basis	Reading
Agentic	19.7Supported · #100/117	33.4Supported · #72/117	Like-for-likeBenchAlign v5.7 lane · 3 vs 13 public rows	Qwen3.6 Plus leads · intervals overlap
Knowledge	33.6Supported · #121/169	52.1Supported · #58/169	Like-for-likeBenchAlign v5.7 lane · 2 vs 8 public rows	Qwen3.6 Plus leads · intervals overlap
Coding	26.6Estimated · #102/143	42.5Supported · #57/143	Directional onlyBenchAlign v5.7 lane · 2 vs 7 public rows	Directional only
Instruction following	82.6#48/124	82.5#50/124	Directional onlyProvisional lane · 0 vs 1 weighted rows	Directional only
Reasoning	69.9Unranked · 2 rankable rows	61.3Unranked · 4 rankable rows	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multimodal	56.7Unranked · 1 rankable row	67.2#24/50	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multilingual	Not ranked	69.7#4/12	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Math	Not ranked	62.0#5/7	Not comparableProvisional lane · 0 vs 4 weighted rows	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

Mistral Medium 3.5 128B: $0.00525; 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

Mistral Medium 3.5 128B: $0.0975; 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

Mistral Medium 3.5 128B: $0.405; Fits in one request; Cached input priced at the published list-input rate
Qwen3.6 Plus: API rate not published; Fits in one request; Cached-input rate unavailable

Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate. 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.

SpecificationMistral Medium 3.5 128BQwen3.6 Plus

Context window

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

Mistral Medium 3.5 128B

256K

Qwen3.6 Plus

API model ID

Mistral Medium 3.5 128B

Not sourced

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.

Mistral Medium 3.5 128B

Not published

Qwen3.6 Plus

No comparable hosted API rate

Documented inputs

Mistral Medium 3.5 128B

Not sourced

Qwen3.6 Plus

Not sourced

Documented outputs

Mistral Medium 3.5 128B

Not sourced

Qwen3.6 Plus

Not sourced

Provider availability

Mistral Medium 3.5 128B

Not sourced

Qwen3.6 Plus

Not sourced

Reasoning profile

Mistral Medium 3.5 128B

Reasoning

Qwen3.6 Plus

Reasoning

Weight access

Mistral Medium 3.5 128B

Open Weight

Qwen3.6 Plus

Proprietary

License

Mistral Medium 3.5 128B

Open Weight

Qwen3.6 Plus

Proprietary

Release date

Mistral Medium 3.5 128B

2026-04-29

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 score estimate, 54.44 versus 36.09, but the 90% score intervals overlap.
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, Mistral Medium 3.5 128B or Qwen3.6 Plus?

Qwen3.6 Plus has the higher public score estimate, 54.44 versus 36.09, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Mistral Medium 3.5 128B or Qwen3.6 Plus?

Qwen3.6 Plus scores higher for coding on the public lane, 42.5 to 26.6. Mistral Medium 3.5 128B 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, Mistral Medium 3.5 128B or Qwen3.6 Plus?

Qwen3.6 Plus leads the public agentic tasks lane, 33.4 to 19.7, with Supported evidence for both models, although the 90% intervals overlap.

Which costs less, Mistral Medium 3.5 128B 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, Mistral Medium 3.5 128B or Qwen3.6 Plus?

Qwen3.6 Plus has the larger documented context window: 1M, compared with 256K.

Benchmark evidence

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

Browse raw public benchmark evidence48 rows

Agentic

τ³-bench results
Mistral Medium 3.5 128B91.4%
Source
Qwen3.6 Plus70.7%
Source
Mistral Medium 3.5 128B leads this result
Gert Labs
Shared source
Mistral Medium 3.5 128B39.10%
Qwen3.6 Plus50.60%
Qwen3.6 Plus leads this result
Terminal-Bench 2.1 (Vals)
Mistral Medium 3.5 128B39.0%
Source
Qwen3.6 Plus53.2%
Source
Qwen3.6 Plus leads this result
Terminal-Bench 2.0
Mistral Medium 3.5 128B—
Qwen3.6 Plus61.6%
Source
Not directly comparable
Claw-Eval
Mistral Medium 3.5 128B—
Qwen3.6 Plus58.8%
Source
Not directly comparable
QwenClawBench
Mistral Medium 3.5 128B—
Qwen3.6 Plus57.2%
Source
Not directly comparable
VITA-Bench
Mistral Medium 3.5 128B—
Qwen3.6 Plus44.3%
Source
Not directly comparable
DeepPlanning
Mistral Medium 3.5 128B—
Qwen3.6 Plus41.5%
Source
Not directly comparable
Toolathlon
Mistral Medium 3.5 128B—
Qwen3.6 Plus39.8%
Source
Not directly comparable
MCP Atlas
Mistral Medium 3.5 128B—
Qwen3.6 Plus48.2%
Source
Not directly comparable
MCP-Tasks
Mistral Medium 3.5 128B—
Qwen3.6 Plus74.1%
Source
Not directly comparable
WideResearch
Mistral Medium 3.5 128B—
Qwen3.6 Plus74.3%
Source
Not directly comparable
ResearchClawBench
Mistral Medium 3.5 128B—
Qwen3.6 Plus18.0%
Source
Not directly comparable

Coding

SWE-bench Verified
Mistral Medium 3.5 128B77.6%
Source
Qwen3.6 Plus78.8%
Source
Qwen3.6 Plus leads this result
SWE-bench (Vals)
Mistral Medium 3.5 128B66.4%
Source
Qwen3.6 Plus73.4%
Source
Qwen3.6 Plus leads this result
SWE-bench Pro
Mistral Medium 3.5 128B—
Qwen3.6 Plus56.6%
Source
Not directly comparable
SWE Multilingual
Mistral Medium 3.5 128B—
Qwen3.6 Plus73.8%
Source
Not directly comparable
LiveCodeBench v6
Mistral Medium 3.5 128B—
Qwen3.6 Plus87.1%
Source
Not directly comparable
Vibe Code Bench
Mistral Medium 3.5 128B—
Qwen3.6 Plus25.56%
Source
Not directly comparable
LiveCodeBench (Vals)
Mistral Medium 3.5 128B—
Qwen3.6 Plus86.0%
Source
Not directly comparable

Reasoning

AI-Needle
Mistral Medium 3.5 128B—
Qwen3.6 Plus68.3%
Source
Not directly comparable
LongBench v2
Mistral Medium 3.5 128B—
Qwen3.6 Plus62%
Source
Not directly comparable

Multimodal

MMMU
Mistral Medium 3.5 128B—
Qwen3.6 Plus86.0%
Source
Not directly comparable
MMMU-Pro
Mistral Medium 3.5 128B—
Qwen3.6 Plus78.8%
Source
Not directly comparable
MathVision
Mistral Medium 3.5 128B—
Qwen3.6 Plus88.0%
Source
Not directly comparable
VideoMMMU
Mistral Medium 3.5 128B—
Qwen3.6 Plus84.0%
Source
Not directly comparable
ScreenSpot Pro
Mistral Medium 3.5 128B—
Qwen3.6 Plus68.2%
Source
Not directly comparable
CharXiv
Mistral Medium 3.5 128B—
Qwen3.6 Plus81.5%
Source
Not directly comparable
V*
Mistral Medium 3.5 128B—
Qwen3.6 Plus96.9%
Source
Not directly comparable

Knowledge

GPQA Diamond (Vals)
Mistral Medium 3.5 128B34.8%
Source
Qwen3.6 Plus87.4%
Source
Qwen3.6 Plus leads this result
MMLU-Pro (Vals)
Mistral Medium 3.5 128B75.3%
Source
Qwen3.6 Plus87.7%
Source
Qwen3.6 Plus leads this result
GPQA
Mistral Medium 3.5 128B—
Qwen3.6 Plus90.4%
Source
Not directly comparable
SuperGPQA
Mistral Medium 3.5 128B—
Qwen3.6 Plus71.6%
Source
Not directly comparable
MMLU-Pro
Mistral Medium 3.5 128B—
Qwen3.6 Plus88.5%
Source
Not directly comparable
MMLU-Redux
Mistral Medium 3.5 128B—
Qwen3.6 Plus94.5%
Source
Not directly comparable
C-Eval
Mistral Medium 3.5 128B—
Qwen3.6 Plus93.3%
Source
Not directly comparable
HLE
Mistral Medium 3.5 128B—
Qwen3.6 Plus28.8%
Source
Not directly comparable

Multilingual

MMLU-ProX
Mistral Medium 3.5 128B—
Qwen3.6 Plus84.7%
Source
Not directly comparable
NOVA-63
Mistral Medium 3.5 128B—
Qwen3.6 Plus57.9%
Source
Not directly comparable

Instruction following

IFEval
Mistral Medium 3.5 128B—
Qwen3.6 Plus94.3%
Source
Not directly comparable
IFBench
Mistral Medium 3.5 128B—
Qwen3.6 Plus75.8%
Source
Not directly comparable

Math

AIME26
Mistral Medium 3.5 128B—
Qwen3.6 Plus95.3%
Source
Not directly comparable
HMMT Feb 2025
Mistral Medium 3.5 128B—
Qwen3.6 Plus96.7%
Source
Not directly comparable
HMMT Nov 2025
Mistral Medium 3.5 128B—
Qwen3.6 Plus94.6%
Source
Not directly comparable
HMMT Feb 2026
Mistral Medium 3.5 128B—
Qwen3.6 Plus87.8%
Source
Not directly comparable
MMAnswerBench
Mistral Medium 3.5 128B—
Qwen3.6 Plus83.8%
Source
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Mistral Medium 3.5 128B—
Qwen3.6 Plus26.207%
Source
Not directly comparable
FrontierMath v2 (Tier 4)
Mistral Medium 3.5 128B—
Qwen3.6 Plus8.333%
Source
Not directly comparable

48 public results · 7 shared

Related comparisons

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Last updated September 29, 2026

Mistral Medium 3.5 128B 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

Coding

Instruction following

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, Mistral Medium 3.5 128B or Qwen3.6 Plus?

Which is better for coding, Mistral Medium 3.5 128B or Qwen3.6 Plus?

Which is better for agentic tasks, Mistral Medium 3.5 128B or Qwen3.6 Plus?

Which costs less, Mistral Medium 3.5 128B or Qwen3.6 Plus?

Which has the larger context window, Mistral Medium 3.5 128B or Qwen3.6 Plus?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch Mistral Medium 3.5 128B vs Qwen3.6 Plus