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 Quasar 438B
Updated November 5, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
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
- 12
- Quasar 438B only
- 0
- 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. Quasar 438B has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
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
Quasar 438B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
MiMo-V2.6-Pro and Quasar 438B are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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.
57.1MiMo-V2.6-Pro39.8Quasar 438B
Directional only · BenchAlign v5.8
MiMo-V2.6-Pro 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.
12 MiMo-V2.6-Pro only
2 Shared
2 categories rest 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.
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
Directional only- MiMo-V2.6-Pro
- 56.6
- Estimated · #40/135
- Quasar 438B
- 39.6
- Estimated · #76/135
- Basis
- BenchAlign v5.8 lane · 9 vs 1 public rows
- Reading
- Directional only
Coding
Directional only- MiMo-V2.6-Pro
- 57.1
- Supported · #25/156
- Quasar 438B
- 39.8
- Estimated · #70/156
- Basis
- BenchAlign v5.8 lane · 4 vs 1 public rows
- Reading
- Directional only
Reasoning
Not comparable- MiMo-V2.6-Pro
- 82.5
- Unranked · 2 rankable rows
- Quasar 438B
- 77.4
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- MiMo-V2.6-Pro
- Not ranked
- Quasar 438B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- MiMo-V2.6-Pro
- 62.3
- Supported · #42/177
- Quasar 438B
- Not ranked
- Basis
- BenchAlign v5.8 lane · 0 vs 0 public rows
- Reading
- Not comparable
Multilingual
Not comparable- MiMo-V2.6-Pro
- Not ranked
- Quasar 438B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- MiMo-V2.6-Pro
- Not ranked
- Quasar 438B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- MiMo-V2.6-Pro
- Not ranked
- Quasar 438B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | MiMo-V2.6-Pro | Quasar 438B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 56.6Estimated · #40/135 | 39.6Estimated · #76/135 | Directional onlyBenchAlign v5.8 lane · 9 vs 1 public rows | Directional only |
| Coding | 57.1Supported · #25/156 | 39.8Estimated · #70/156 | Directional onlyBenchAlign v5.8 lane · 4 vs 1 public rows | Directional only |
| Reasoning | 82.5Unranked · 2 rankable rows | 77.4Unranked · 2 rankable rows | 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 | 62.3Supported · #42/177 | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 0 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
- Quasar 438B
- $0.0015
- Fits in one request
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
- Quasar 438B
- $0.0354
- Fits in one request
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
- Quasar 438B
- $0.15
- Fits in one request
- Cached input priced at the published list-input rate
MiMo-V2.6-Pro has the lower modeled cost
Quasar 438B 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.
SpecificationMiMo-V2.6-ProQuasar 438B
Context window
Maximum documented context; output-token limits may be lower.
MiMo-V2.6-Pro
Quasar 438B
API model ID
MiMo-V2.6-Pro
Not sourced
Quasar 438B
quasar-438b
CompactifAI Quasar 438B documentationCached-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 launchQuasar 438B
Not published
CompactifAI API pricingDocumented inputs
MiMo-V2.6-Pro
Not sourced
Quasar 438B
Not sourced
Documented outputs
MiMo-V2.6-Pro
Not sourced
Quasar 438B
Not sourced
Provider availability
MiMo-V2.6-Pro
Not sourced
Quasar 438B
Generally Available · CompactifAI API
CompactifAI Quasar 438B documentationReasoning profile
MiMo-V2.6-Pro
Reasoning
Quasar 438B
Reasoning
Weight access
MiMo-V2.6-Pro
Open Weight
Quasar 438B
Proprietary
License
MiMo-V2.6-Pro
Open Weight
Quasar 438B
Proprietary
Release date
MiMo-V2.6-Pro
2026-09-22
Quasar 438B
2026-09-02
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
- 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 $0.0354. Cache-heavy agent loop: $0.01812 vs $0.15.
- Context tradeoff
- Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, MiMo-V2.6-Pro or Quasar 438B?
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 Quasar 438B?
MiMo-V2.6-Pro scores higher for coding on the public lane, 57.1 to 39.8. Quasar 438B 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, MiMo-V2.6-Pro or Quasar 438B?
MiMo-V2.6-Pro scores higher for agentic tasks on the public lane, 56.6 to 39.6. MiMo-V2.6-Pro and Quasar 438B are scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, MiMo-V2.6-Pro or Quasar 438B?
For the stated presets, chat costs $0.00087 on MiMo-V2.6-Pro and $0.0015 on Quasar 438B; repository review costs $0.02436 and $0.0354; the cache-heavy agent loop costs $0.01812 and $0.15. Quasar 438B has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, MiMo-V2.6-Pro or Quasar 438B?
Both models list the same context window, 1M.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
MiMo-V2.6-Pro
API / mo$979
Self-host / mo$37,843
Break-even2.2B/day
Quasar 438B
API / mo$1,800
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence14 rows
Agentic
Toolathlon-Verified
MiMo-V2.6-Pro76.9%SourceQuasar 438B—Not directly comparable
AutomationBench
MiMo-V2.6-Pro53.1%SourceQuasar 438B—Not directly comparable
Agents' Last Exam
MiMo-V2.6-Pro31.6%SourceQuasar 438B—Not directly comparable
Terminal-Bench 4.0
MiMo-V2.6-Pro34.90%SourceQuasar 438B—Not directly comparable
Terminal-Bench 2.1
MiMo-V2.6-Pro89.9%SourceQuasar 438B69.3%SourceMiMo-V2.6-Pro leads this result
OSWorld-Verified
MiMo-V2.6-Pro82%SourceQuasar 438B—Not directly comparable
JobBench
MiMo-V2.6-Pro62.0%SourceQuasar 438B—Not directly comparable
CyberGym
MiMo-V2.6-Pro94.0%SourceQuasar 438B—Not directly comparable
ExploitGym
MiMo-V2.6-Pro17.8%SourceQuasar 438B—Not directly comparable
Coding
DeepSWE
MiMo-V2.6-Pro71.9%SourceQuasar 438B—Not directly comparable
ProgramBench
MiMo-V2.6-Pro26.5%SourceQuasar 438B—Not directly comparable
Terminal-Bench 2.1
MiMo-V2.6-Pro89.9%SourceQuasar 438B69.3%SourceMiMo-V2.6-Pro leads this result
Bug Hunt Bench
MiMo-V2.6-Pro22.7 fixesSourceQuasar 438B—Not directly comparable
Reasoning
STEMBench
MiMo-V2.6-Pro2.17SourceQuasar 438B—Not directly comparable
14 public results · 2 shared
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Last updated November 5, 2026