Agentic
Not comparable- GPT-5.2-Codex
- 49.8
- Estimated · #61/154
- MiMo-V2.6-Pro
- Not ranked
- Basis
- BenchAlign lane · 2 vs 9 public rows
- Reading
- Not comparable
GPT-5.2-Codex vs MiMo-V2.6-Pro
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.
1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Updated September 21, 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
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.
Long documents
Prompts that approach the documented context limit
MiMo-V2.6-Pro
MiMo-V2.6-Pro has the larger documented context window.
Confidence: documented
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
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. GPT-5.2-Codex 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-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
MiMo-V2.6-Pro is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
MiMo-V2.6-Pro is not ranked on the public lane for agentic, so no winner is named for agentic.
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.
52.4GPT-5.2-Codex—MiMo-V2.6-Pro
Not comparable · BenchAlign
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.
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
4 GPT-5.2-Codex only
1 Shared
11 MiMo-V2.6-Pro only
- Shared results
- 1
- GPT-5.2-Codex only
- 4
- MiMo-V2.6-Pro only
- 11
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign 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.
Coding
Not comparable- GPT-5.2-Codex
- 52.4
- Supported · #51/156
- MiMo-V2.6-Pro
- Not ranked
- Basis
- BenchAlign lane · 3 vs 3 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.2-Codex
- 77.6
- Unranked · 2 rankable rows
- MiMo-V2.6-Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.2-Codex
- 72.5
- Unranked · 1 rankable row
- MiMo-V2.6-Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5.2-Codex
- 55.8
- Estimated · #51/186
- MiMo-V2.6-Pro
- Not ranked
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.2-Codex
- Not ranked
- MiMo-V2.6-Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.2-Codex
- 92.4
- #3/124
- MiMo-V2.6-Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.2-Codex
- Not ranked
- MiMo-V2.6-Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.2-Codex | MiMo-V2.6-Pro | Basis | Reading |
|---|---|---|---|---|
| Agentic | 49.8Estimated · #61/154 | Not ranked | Not comparableBenchAlign lane · 2 vs 9 public rows | Not comparable |
| Coding | 52.4Supported · #51/156 | Not ranked | Not comparableBenchAlign lane · 3 vs 3 public rows | Not comparable |
| Reasoning | 77.6Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 72.5Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 55.8Estimated · #51/186 | Not ranked | Not comparableBenchAlign 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 | 92.4#3/124 | 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) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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
- GPT-5.2-Codex
- $0.00875
- Fits in one request
- MiMo-V2.6-Pro
- $0.00087
- 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
- GPT-5.2-Codex
- $0.1295
- Fits in one request
- MiMo-V2.6-Pro
- $0.02436
- 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
- GPT-5.2-Codex
- $0.525
- Fits in one request
- Cached input priced at the published list-input rate
- MiMo-V2.6-Pro
- $0.01812
- Fits in one request
MiMo-V2.6-Pro has the lower modeled cost
GPT-5.2-Codex has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.2-CodexMiMo-V2.6-Pro
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.2-Codex
400K
MiMo-V2.6-Pro
API model ID
GPT-5.2-Codex
Not sourced
MiMo-V2.6-Pro
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.2-Codex
Not published
MiMo-V2.6-Pro
$0.0036 per 1M cached input tokens
Xiaomi MiMo-V2.6 launchDocumented inputs
GPT-5.2-Codex
Not sourced
MiMo-V2.6-Pro
Not sourced
Documented outputs
GPT-5.2-Codex
Not sourced
MiMo-V2.6-Pro
Not sourced
Provider availability
GPT-5.2-Codex
Not sourced
MiMo-V2.6-Pro
Not sourced
Reasoning profile
GPT-5.2-Codex
Reasoning
MiMo-V2.6-Pro
Reasoning
Weight access
GPT-5.2-Codex
Proprietary
MiMo-V2.6-Pro
Open Weight
License
GPT-5.2-Codex
Proprietary
MiMo-V2.6-Pro
Open Weight
Release date
GPT-5.2-Codex
2025-12-18
MiMo-V2.6-Pro
2026-09-22
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: $0.1295 vs $0.02436. Cache-heavy agent loop: $0.525 vs $0.01812.
- Context tradeoff
- MiMo-V2.6-Pro has the larger documented window (1M).
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 evidence16 rows
Agentic
Gert Labs
GPT-5.2-Codex51.79%SourceMiMo-V2.6-Pro—Not directly comparable
JobBench
GPT-5.2-Codex26.0%SourceMiMo-V2.6-Pro62.0%SourceMiMo-V2.6-Pro leads this result
Toolathlon-Verified
GPT-5.2-Codex—MiMo-V2.6-Pro76.9%SourceNot directly comparable
AutomationBench
GPT-5.2-Codex—MiMo-V2.6-Pro53.1%SourceNot directly comparable
Agents' Last Exam
GPT-5.2-Codex—MiMo-V2.6-Pro31.6%SourceNot directly comparable
Terminal-Bench 4.0
GPT-5.2-Codex—MiMo-V2.6-Pro34.90%SourceNot directly comparable
Terminal-Bench 2.1
GPT-5.2-Codex—MiMo-V2.6-Pro89.9%SourceNot directly comparable
OSWorld-Verified
GPT-5.2-Codex—MiMo-V2.6-Pro82%SourceNot directly comparable
CyberGym
GPT-5.2-Codex—MiMo-V2.6-Pro94.0%SourceNot directly comparable
ExploitGym
GPT-5.2-Codex—MiMo-V2.6-Pro17.8%SourceNot directly comparable
Coding
Vibe Code Bench
GPT-5.2-Codex37.91%SourceMiMo-V2.6-Pro—Not directly comparable
LiveCodeBench (Vals)
GPT-5.2-Codex88.0%SourceMiMo-V2.6-Pro—Not directly comparable
SWE-bench (Vals)
GPT-5.2-Codex72.4%SourceMiMo-V2.6-Pro—Not directly comparable
DeepSWE
GPT-5.2-Codex—MiMo-V2.6-Pro71.9%SourceNot directly comparable
ProgramBench
GPT-5.2-Codex—MiMo-V2.6-Pro26.5%SourceNot directly comparable
Terminal-Bench 2.1
GPT-5.2-Codex—MiMo-V2.6-Pro89.9%SourceNot directly comparable
Questions
Which is better, GPT-5.2-Codex or MiMo-V2.6-Pro?
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, GPT-5.2-Codex or MiMo-V2.6-Pro?
MiMo-V2.6-Pro is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT-5.2-Codex or MiMo-V2.6-Pro?
MiMo-V2.6-Pro is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-5.2-Codex or MiMo-V2.6-Pro?
For the stated presets, chat costs $0.00875 on GPT-5.2-Codex and $0.00087 on MiMo-V2.6-Pro; repository review costs $0.1295 and $0.02436; the cache-heavy agent loop costs $0.525 and $0.01812. GPT-5.2-Codex has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-5.2-Codex or MiMo-V2.6-Pro?
MiMo-V2.6-Pro has the larger documented context window: 1M, compared with 400K.
Related comparisons
Last updated September 21, 2026
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