Agentic
Not comparable- Mistral Medium 3.5 128B
- Not measured
- Qwen 3.6 Max (preview)
- 65.4
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Model comparison
Mistral Medium 3.5 128B vs Qwen 3.6 Max (preview)
Updated July 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.
0 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 Mistral Medium 3.5 128B only
9 Qwen 3.6 Max (preview) only
- Shared results
- 0
- Mistral Medium 3.5 128B only
- 3
- Qwen 3.6 Max (preview) only
- 9
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Not comparable- Mistral Medium 3.5 128B
- 77.6
- Qwen 3.6 Max (preview)
- 51.0
- Weighted basis
- 1 vs 2 rows
- Reading
- Not comparable
Reasoning
Not comparable- Mistral Medium 3.5 128B
- Not measured
- Qwen 3.6 Max (preview)
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- Mistral Medium 3.5 128B
- Not measured
- Qwen 3.6 Max (preview)
- 73.9
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Math
Not comparable- Mistral Medium 3.5 128B
- Not measured
- Qwen 3.6 Max (preview)
- 18.4
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Multilingual
Not comparable- Mistral Medium 3.5 128B
- Not measured
- Qwen 3.6 Max (preview)
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Mistral Medium 3.5 128B
- Not measured
- Qwen 3.6 Max (preview)
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Mistral Medium 3.5 128B
- Not measured
- Qwen 3.6 Max (preview)
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Mistral Medium 3.5 128B | Qwen 3.6 Max (preview) | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | Not measured | 65.4 | Not comparable0 vs 1 rows | Not comparable |
| Coding | 77.6 | 51.0 | Not comparable1 vs 2 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | 73.9 | Not comparable0 vs 1 rows | Not comparable |
| Math | Not measured | 18.4 | Not comparable0 vs 2 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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
- Qwen 3.6 Max (preview)
- API rate not published
- Fits in one request
Qwen 3.6 Max (preview) 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
- Qwen 3.6 Max (preview)
- API rate not published
- Fits in one request
Qwen 3.6 Max (preview) 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
- Qwen 3.6 Max (preview)
- 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. Qwen 3.6 Max (preview) has no comparable published API token rate.
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 128BQwen 3.6 Max (preview)
Context window
Maximum documented context; output-token limits may be lower.
Mistral Medium 3.5 128B
256K
Qwen 3.6 Max (preview)
256K
API model ID
Mistral Medium 3.5 128B
Not sourced
Qwen 3.6 Max (preview)
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
Qwen 3.6 Max (preview)
No comparable hosted API rate
Documented inputs
Mistral Medium 3.5 128B
Not sourced
Qwen 3.6 Max (preview)
Not sourced
Documented outputs
Mistral Medium 3.5 128B
Not sourced
Qwen 3.6 Max (preview)
Not sourced
Provider availability
Mistral Medium 3.5 128B
Not sourced
Qwen 3.6 Max (preview)
Not sourced
Reasoning profile
Mistral Medium 3.5 128B
Reasoning
Qwen 3.6 Max (preview)
Reasoning
Weight access
Mistral Medium 3.5 128B
Open Weight
Qwen 3.6 Max (preview)
Proprietary
License
Mistral Medium 3.5 128B
Open Weight
Qwen 3.6 Max (preview)
Proprietary
Release date
Mistral Medium 3.5 128B
2026-04-29
Qwen 3.6 Max (preview)
2026-04-20
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Both models list 256K.
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence12 rows
Agentic
τ³-bench results
Mistral Medium 3.5 128B91.4%SourceQwen 3.6 Max (preview)—Not directly comparable
Gert Labs
Mistral Medium 3.5 128B39.10%SourceQwen 3.6 Max (preview)—Not directly comparable
Terminal-Bench 2.0
Mistral Medium 3.5 128B—Qwen 3.6 Max (preview)65.4%SourceNot directly comparable
QwenClawBench
Mistral Medium 3.5 128B—Qwen 3.6 Max (preview)59.0%SourceNot directly comparable
Coding
SWE-bench Verified
Mistral Medium 3.5 128B77.6%SourceQwen 3.6 Max (preview)—Not directly comparable
SWE-bench Pro
Mistral Medium 3.5 128B—Qwen 3.6 Max (preview)57.3%SourceNot directly comparable
SciCode
Mistral Medium 3.5 128B—Qwen 3.6 Max (preview)47%SourceNot directly comparable
NL2Repo
Mistral Medium 3.5 128B—Qwen 3.6 Max (preview)42.9%SourceNot directly comparable
Terminal-Bench 2.0
Mistral Medium 3.5 128B—Qwen 3.6 Max (preview)65.4%SourceNot directly comparable
Knowledge
SuperGPQA
Mistral Medium 3.5 128B—Qwen 3.6 Max (preview)73.9%SourceNot directly comparable
Math
Frequently asked questions
Which is better, Mistral Medium 3.5 128B or Qwen 3.6 Max (preview)?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Mistral Medium 3.5 128B or Qwen 3.6 Max (preview)?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, Mistral Medium 3.5 128B or Qwen 3.6 Max (preview)?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, Mistral Medium 3.5 128B or Qwen 3.6 Max (preview)?
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 Qwen 3.6 Max (preview)?
Both models list the same context window, 256K.
Related comparisons
Last updated July 30, 2026
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