Chat turn cost
1K fresh input + 500 output tokens
MiMo-V2.6-Pro
MiMo-V2.6-Pro has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
MiMo-V2.6-Pro vs Pareto 26.10 Preview
Updated October 1, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 2
- MiMo-V2.6-Pro only
- 10
- Pareto 26.10 Preview only
- 1
- Like-for-like categories
- 0 / 8
Estimated: MiMo-V2.6-Pro. 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.
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
MiMo-V2.6-Pro
MiMo-V2.6-Pro 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
Repository review cost
50K fresh input + 3K output tokens
MiMo-V2.6-Pro
MiMo-V2.6-Pro has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Pareto 26.10 Preview is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Pareto 26.10 Preview is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
Not enough matched evidence
A complete context comparison is not sourced.
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.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
68.0MiMo-V2.6-Pro—Pareto 26.10 Preview
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
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.
10 MiMo-V2.6-Pro only
2 Shared
1 Pareto 26.10 Preview only
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.
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
Not comparable- MiMo-V2.6-Pro
- 66.4
- Estimated · #13/119
- Pareto 26.10 Preview
- Not ranked
- Basis
- BenchAlign v5.8 lane · 9 vs 1 public rows
- Reading
- Not comparable
Coding
Not comparable- MiMo-V2.6-Pro
- 68.0
- Supported · #9/144
- Pareto 26.10 Preview
- Not ranked
- Basis
- BenchAlign v5.8 lane · 3 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- MiMo-V2.6-Pro
- 79.6
- Unranked · 2 rankable rows
- Pareto 26.10 Preview
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- MiMo-V2.6-Pro
- Not ranked
- Pareto 26.10 Preview
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- MiMo-V2.6-Pro
- 68.2
- Supported · #17/170
- Pareto 26.10 Preview
- Not ranked
- Basis
- BenchAlign v5.8 lane · 0 vs 1 public rows
- Reading
- Not comparable
Multilingual
Not comparable- MiMo-V2.6-Pro
- Not ranked
- Pareto 26.10 Preview
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- MiMo-V2.6-Pro
- Not ranked
- Pareto 26.10 Preview
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- MiMo-V2.6-Pro
- Not ranked
- Pareto 26.10 Preview
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | MiMo-V2.6-Pro | Pareto 26.10 Preview | Basis | Reading |
|---|---|---|---|---|
| Agentic | 66.4Estimated · #13/119 | Not ranked | Not comparableBenchAlign v5.8 lane · 9 vs 1 public rows | Not comparable |
| Coding | 68.0Supported · #9/144 | Not ranked | Not comparableBenchAlign v5.8 lane · 3 vs 1 public rows | Not comparable |
| Reasoning | 79.6Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 68.2Supported · #17/170 | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 1 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) 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
- MiMo-V2.6-Pro
- $0.00087
- Fits in one request
- Pareto 26.10 Preview
- $0.00625
- Fit state unavailable
MiMo-V2.6-Pro has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- MiMo-V2.6-Pro
- $0.02436
- Fits in one request
- Pareto 26.10 Preview
- $0.1475
- Fit state unavailable
MiMo-V2.6-Pro has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- MiMo-V2.6-Pro
- $0.01812
- Fits in one request
- Pareto 26.10 Preview
- $0.175
- Fit state unavailable
MiMo-V2.6-Pro has the lower modeled cost
Costs use the listed standard API rates.
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.
SpecificationMiMo-V2.6-ProPareto 26.10 Preview
Context window
Maximum documented context; output-token limits may be lower.
MiMo-V2.6-Pro
Pareto 26.10 Preview
Not sourced
Unbiased Pareto 26.10 Preview model cardAPI model ID
MiMo-V2.6-Pro
Not sourced
Pareto 26.10 Preview
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
MiMo-V2.6-Pro
$0.0036 per 1M cached input tokens
Xiaomi MiMo-V2.6 launchPareto 26.10 Preview
$0.25 per 1M cached input tokens
Unbiased direct API pricingDocumented inputs
MiMo-V2.6-Pro
Not sourced
Pareto 26.10 Preview
text, image
OpenRouter Pareto 26.10 Preview routeDocumented outputs
MiMo-V2.6-Pro
Not sourced
Pareto 26.10 Preview
Provider availability
MiMo-V2.6-Pro
Not sourced
Pareto 26.10 Preview
Preview · Unbiased Chat Completions API, Unbiased Messages API, OpenRouter
Unbiased API documentationReasoning profile
MiMo-V2.6-Pro
Reasoning
Pareto 26.10 Preview
Reasoning
Weight access
MiMo-V2.6-Pro
Open Weight
Pareto 26.10 Preview
Proprietary
License
MiMo-V2.6-Pro
Open Weight
Pareto 26.10 Preview
Proprietary
Release date
MiMo-V2.6-Pro
2026-09-22
Pareto 26.10 Preview
2026-10-01
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- Repository review: $0.02436 vs $0.1475. Cache-heavy agent loop: $0.01812 vs $0.175.
- Context tradeoff
- A complete documented context comparison is not available.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, MiMo-V2.6-Pro or Pareto 26.10 Preview?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, MiMo-V2.6-Pro or Pareto 26.10 Preview?
Pareto 26.10 Preview is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, MiMo-V2.6-Pro or Pareto 26.10 Preview?
Pareto 26.10 Preview is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, MiMo-V2.6-Pro or Pareto 26.10 Preview?
For the stated presets, chat costs $0.00087 on MiMo-V2.6-Pro and $0.00625 on Pareto 26.10 Preview; repository review costs $0.02436 and $0.1475; the cache-heavy agent loop costs $0.01812 and $0.175. Costs use the listed standard API rates.
Which has the larger context window, MiMo-V2.6-Pro or Pareto 26.10 Preview?
A complete documented context-window comparison is not available.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence13 rows
Agentic
Toolathlon-Verified
MiMo-V2.6-Pro76.9%SourcePareto 26.10 Preview—Not directly comparable
AutomationBench
MiMo-V2.6-Pro53.1%SourcePareto 26.10 Preview—Not directly comparable
Agents' Last Exam
MiMo-V2.6-Pro31.6%SourcePareto 26.10 Preview—Not directly comparable
Terminal-Bench 4.0
MiMo-V2.6-Pro34.90%SourcePareto 26.10 Preview50.80%SourcePareto 26.10 Preview leads this result
Terminal-Bench 2.1
MiMo-V2.6-Pro89.9%SourcePareto 26.10 Preview—Not directly comparable
OSWorld-Verified
MiMo-V2.6-Pro82%SourcePareto 26.10 Preview—Not directly comparable
JobBench
MiMo-V2.6-Pro62.0%SourcePareto 26.10 Preview—Not directly comparable
CyberGym
MiMo-V2.6-Pro94.0%SourcePareto 26.10 Preview—Not directly comparable
ExploitGym
MiMo-V2.6-Pro17.8%SourcePareto 26.10 Preview—Not directly comparable
Coding
Knowledge
GPQA-D
MiMo-V2.6-Pro—Pareto 26.10 Preview92.4%SourceNot directly comparable
13 public results · 2 shared
Watch MiMo-V2.6-Pro vs Pareto 26.10 Preview
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.
Last updated October 1, 2026