Claude Opus 4.6 vs Mistral Medium 3.5 128B

Updated September 29, 2026. Rank says Claude Opus 4.6 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Claude Opus 4.6 has the higher public score, 63.49 versus 36.09, and the 90% score intervals do not overlap. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Claude Opus 4.6

Anthropic

63.49/100

Supported · Public rank #37

90% interval 51.3–75.7

Model B

Mistral Medium 3.5 128B

Mistral

36.09/100

Estimated · Public rank #137

90% interval 24.6–47.6

Shared results: 2
Claude Opus 4.6 only: 32
Mistral Medium 3.5 128B only: 5
Like-for-like categories: 1 / 8

Supported: Claude Opus 4.6 · Estimated: Mistral Medium 3.5 128BHow 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
Claude Opus 4.6
Claude Opus 4.6 leads on the public agentic lane, 44.5 to 19.7, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Long documents
Prompts that approach the documented context limit
Claude Opus 4.6
Claude Opus 4.6 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Mistral Medium 3.5 128B
Mistral Medium 3.5 128B has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates

Show secondary and unsupported calls

Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Mistral Medium 3.5 128B
Mistral Medium 3.5 128B has the lower estimated token cost for this stated workload. Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate. Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
Mistral Medium 3.5 128B
Mistral Medium 3.5 128B has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
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

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.

49.7Claude Opus 4.626.6Mistral Medium 3.5 128B

Directional only · BenchAlign v5.7

Claude Opus 4.6 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.

SWE-bench VerifiedCoding
Normalized gap 3.2
Claude Opus 4.6:80.8%
Mistral Medium 3.5 128B:77.6%
Claude Opus 4.6 source Mistral Medium 3.5 128B 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

Claude Opus 4.6: 44.5; Supported · #44/117
Mistral Medium 3.5 128B: 19.7; Supported · #100/117
Basis: BenchAlign v5.7 lane · 10 vs 3 public rows
Reading: Claude Opus 4.6 leads

Coding

Directional only

Claude Opus 4.6: 49.7; Supported · #42/143
Mistral Medium 3.5 128B: 26.6; Estimated · #102/143
Basis: BenchAlign v5.7 lane · 8 vs 2 public rows
Reading: Directional only

Knowledge

Directional only

Claude Opus 4.6: 58.4; Estimated · #43/169
Mistral Medium 3.5 128B: 33.6; Supported · #121/169
Basis: BenchAlign v5.7 lane · 9 vs 2 public rows
Reading: Directional only

Instruction following

Directional only

Claude Opus 4.6: 51.0; #79/124
Mistral Medium 3.5 128B: 82.6; #48/124
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

Claude Opus 4.6: 68.3; Unranked · 2 rankable rows
Mistral Medium 3.5 128B: 69.9; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Claude Opus 4.6: 60.6; #30/50
Mistral Medium 3.5 128B: 56.7; Unranked · 1 rankable row
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Claude Opus 4.6: Not ranked
Mistral Medium 3.5 128B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Claude Opus 4.6: 58.5; Unranked · 3 rankable rows
Mistral Medium 3.5 128B: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Claude Opus 4.6 and Mistral Medium 3.5 128B
Category	Claude Opus 4.6	Mistral Medium 3.5 128B	Basis	Reading
Agentic	44.5Supported · #44/117	19.7Supported · #100/117	Like-for-likeBenchAlign v5.7 lane · 10 vs 3 public rows	Claude Opus 4.6 leads
Coding	49.7Supported · #42/143	26.6Estimated · #102/143	Directional onlyBenchAlign v5.7 lane · 8 vs 2 public rows	Directional only
Knowledge	58.4Estimated · #43/169	33.6Supported · #121/169	Directional onlyBenchAlign v5.7 lane · 9 vs 2 public rows	Directional only
Instruction following	51.0#79/124	82.6#48/124	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Reasoning	68.3Unranked · 2 rankable rows	69.9Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	60.6#30/50	56.7Unranked · 1 rankable row	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	58.5Unranked · 3 rankable rows	Not ranked	Not comparableProvisional lane · 2 vs 0 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

Claude Opus 4.6: $0.0175; Fits in one request
Mistral Medium 3.5 128B: $0.00525; Fits in one request

Mistral Medium 3.5 128B has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Opus 4.6: $0.325; Fits in one request
Mistral Medium 3.5 128B: $0.0975; Fits in one request

Mistral Medium 3.5 128B has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Claude Opus 4.6: $1.35; Fits in one request; Cached input priced at the published list-input rate
Mistral Medium 3.5 128B: $0.405; Fits in one request; Cached input priced at the published list-input rate

Mistral Medium 3.5 128B has the lower modeled cost

Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate. Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input 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.

SpecificationClaude Opus 4.6Mistral Medium 3.5 128B

Context window

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

Claude Opus 4.6

Mistral Medium 3.5 128B

256K

API model ID

Claude Opus 4.6

Not sourced

Mistral Medium 3.5 128B

Not sourced

Cached-input rate

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

Claude Opus 4.6

Not published

Mistral Medium 3.5 128B

Not published

Documented inputs

Claude Opus 4.6

Not sourced

Mistral Medium 3.5 128B

Not sourced

Documented outputs

Claude Opus 4.6

Not sourced

Mistral Medium 3.5 128B

Not sourced

Provider availability

Claude Opus 4.6

Not sourced

Mistral Medium 3.5 128B

Not sourced

Reasoning profile

Claude Opus 4.6

Non-Reasoning

Mistral Medium 3.5 128B

Reasoning

Weight access

Claude Opus 4.6

Proprietary

Mistral Medium 3.5 128B

Open Weight

License

Claude Opus 4.6

Proprietary

Mistral Medium 3.5 128B

Open Weight

Release date

Claude Opus 4.6

2026-02-01

Mistral Medium 3.5 128B

2026-04-29

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: Claude Opus 4.6 has the higher public score, 63.49 versus 36.09, and the 90% score intervals do not overlap.
Workload cost: Repository review: $0.325 vs $0.0975. Cache-heavy agent loop: $1.35 vs $0.405.
Context tradeoff: Claude Opus 4.6 has the larger documented window (1M).

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

Questions

Which is better, Claude Opus 4.6 or Mistral Medium 3.5 128B?

Claude Opus 4.6 has the higher public score, 63.49 versus 36.09, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, Claude Opus 4.6 or Mistral Medium 3.5 128B?

Claude Opus 4.6 scores higher for coding on the public lane, 49.7 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, Claude Opus 4.6 or Mistral Medium 3.5 128B?

Claude Opus 4.6 leads the public agentic tasks lane, 44.5 to 19.7, with Supported evidence for both models and non-overlapping 90% intervals.

Which costs less, Claude Opus 4.6 or Mistral Medium 3.5 128B?

For the stated presets, chat costs $0.0175 on Claude Opus 4.6 and $0.00525 on Mistral Medium 3.5 128B; repository review costs $0.325 and $0.0975; the cache-heavy agent loop costs $1.35 and $0.405. Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate. Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Opus 4.6 or Mistral Medium 3.5 128B?

Claude Opus 4.6 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 evidence39 rows

Agentic

Terminal-Bench 2.0
Claude Opus 4.665.4%
Source
Mistral Medium 3.5 128B—
Not directly comparable
BrowseComp
Claude Opus 4.683.7%
Source
Mistral Medium 3.5 128B—
Not directly comparable
OSWorld-Verified
Claude Opus 4.672.7%
Source
Mistral Medium 3.5 128B—
Not directly comparable
Claw-Eval
Claude Opus 4.670.4%
Source
Mistral Medium 3.5 128B—
Not directly comparable
DeepSearchQA
Claude Opus 4.673.7%
Source
Mistral Medium 3.5 128B—
Not directly comparable
CyberGym
Claude Opus 4.666.6%
Source
Mistral Medium 3.5 128B—
Not directly comparable
Gert Labs
Shared source
Claude Opus 4.661.85%
Mistral Medium 3.5 128B39.10%
Claude Opus 4.6 leads this result
ResearchClawBench
Claude Opus 4.619.9%
Source
Mistral Medium 3.5 128B—
Not directly comparable
JobBench
Claude Opus 4.636.7%
Source
Mistral Medium 3.5 128B—
Not directly comparable
ApprenticeBench
Claude Opus 4.65%
Source
Mistral Medium 3.5 128B—
Not directly comparable
τ³-bench results
Claude Opus 4.6—
Mistral Medium 3.5 128B91.4%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Opus 4.6—
Mistral Medium 3.5 128B39.0%
Source
Not directly comparable

Coding

SWE-bench Verified
Claude Opus 4.680.8%
Source
Mistral Medium 3.5 128B77.6%
Source
Claude Opus 4.6 leads this result
SWE-bench Verified*
Claude Opus 4.675.6%
Source
Mistral Medium 3.5 128B—
Not directly comparable
LiveCodeBench Pro
Claude Opus 4.670.7%
Source
Mistral Medium 3.5 128B—
Not directly comparable
SWE-bench Pro
Claude Opus 4.653.4%
Source
Mistral Medium 3.5 128B—
Not directly comparable
SWE-Rebench
Claude Opus 4.665.3%
Source
Mistral Medium 3.5 128B—
Not directly comparable
React Native Evals
Claude Opus 4.684.1%
Source
Mistral Medium 3.5 128B—
Not directly comparable
Vibe Code Bench
Claude Opus 4.657.57%
Source
Mistral Medium 3.5 128B—
Not directly comparable
FrontierCode 1.1 Main
Claude Opus 4.626.9%
Source
Mistral Medium 3.5 128B—
Not directly comparable
SWE-bench (Vals)
Claude Opus 4.6—
Mistral Medium 3.5 128B66.4%
Source
Not directly comparable

Multimodal

MMMU-Pro
Claude Opus 4.677.3%
Source
Mistral Medium 3.5 128B—
Not directly comparable
ERQA
Claude Opus 4.651.6%
Source
Mistral Medium 3.5 128B—
Not directly comparable
ScreenSpot Pro
Claude Opus 4.683.1%
Source
Mistral Medium 3.5 128B—
Not directly comparable
MedXpertQA (MM)
Claude Opus 4.664.8%
Source
Mistral Medium 3.5 128B—
Not directly comparable

Knowledge

GPQA
Claude Opus 4.691.3%
Source
Mistral Medium 3.5 128B—
Not directly comparable
GPQA-D
Claude Opus 4.689.2%
Source
Mistral Medium 3.5 128B—
Not directly comparable
SuperGPQA
Claude Opus 4.695%
Source
Mistral Medium 3.5 128B—
Not directly comparable
MMLU-Pro
Claude Opus 4.682%
Source
Mistral Medium 3.5 128B—
Not directly comparable
MMLU-Pro (Arcee)
Claude Opus 4.689.1%
Source
Mistral Medium 3.5 128B—
Not directly comparable
HLE
Claude Opus 4.653%
Source
Mistral Medium 3.5 128B—
Not directly comparable
HLE w/o tools
Claude Opus 4.640%
Source
Mistral Medium 3.5 128B—
Not directly comparable
HealthBench Hard
Claude Opus 4.614.8%
Source
Mistral Medium 3.5 128B—
Not directly comparable
MedXpertQA (Text)
Claude Opus 4.652.1%
Source
Mistral Medium 3.5 128B—
Not directly comparable
GPQA Diamond (Vals)
Claude Opus 4.6—
Mistral Medium 3.5 128B34.8%
Source
Not directly comparable
MMLU-Pro (Vals)
Claude Opus 4.6—
Mistral Medium 3.5 128B75.3%
Source
Not directly comparable

Math

AIME25 (Arcee)
Claude Opus 4.699.8%
Source
Mistral Medium 3.5 128B—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Claude Opus 4.640.700%
Source
Mistral Medium 3.5 128B—
Not directly comparable
FrontierMath v2 (Tier 4)
Claude Opus 4.622.900%
Source
Mistral Medium 3.5 128B—
Not directly comparable

39 public results · 2 shared

Related comparisons

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Last updated September 29, 2026

Claude Opus 4.6 vs Mistral Medium 3.5 128B

Decision reading

Which one for your work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Coding work

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Knowledge

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, Claude Opus 4.6 or Mistral Medium 3.5 128B?

Which is better for coding, Claude Opus 4.6 or Mistral Medium 3.5 128B?

Which is better for agentic tasks, Claude Opus 4.6 or Mistral Medium 3.5 128B?

Which costs less, Claude Opus 4.6 or Mistral Medium 3.5 128B?

Which has the larger context window, Claude Opus 4.6 or Mistral Medium 3.5 128B?

Benchmark evidence

Agentic

Coding

Multimodal

Knowledge

Math

Related comparisons

Watch Claude Opus 4.6 vs Mistral Medium 3.5 128B