Long documents
Prompts that approach the documented context limit
Kimi K2.6
Kimi K2.6 has the larger documented context window.
Confidence: documented
GPT Realtime mini vs Kimi K2.6
Updated October 29, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
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Decision reading
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 resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 0
- GPT Realtime mini only
- 0
- Kimi K2.6 only
- 37
- Like-for-like categories
- 0 / 8
Estimated: Kimi K2.6. 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.
Chat turn cost
1K fresh input + 500 output tokens
GPT Realtime mini
GPT Realtime mini 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
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GPT Realtime mini 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
GPT Realtime mini is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GPT Realtime mini does not fit this workload in one request.
Confidence: listed-ratesRepository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GPT Realtime mini does not fit this workload in one request.
Confidence: listed-rates
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.
—GPT Realtime mini42.9Kimi K2.6
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.
37 Kimi K2.6 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- GPT Realtime mini
- Not ranked
- Kimi K2.6
- 44.5
- Supported · #67/135
- Basis
- BenchAlign v5.8 lane · 0 vs 12 public rows
- Reading
- Not comparable
Coding
Not comparable- GPT Realtime mini
- Not ranked
- Kimi K2.6
- 42.9
- Supported · #63/156
- Basis
- BenchAlign v5.8 lane · 0 vs 10 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT Realtime mini
- Not ranked
- Kimi K2.6
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT Realtime mini
- Not ranked
- Kimi K2.6
- 73.5
- #30/58
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT Realtime mini
- Not ranked
- Kimi K2.6
- 58.8
- Supported · #52/179
- Basis
- BenchAlign v5.8 lane · 0 vs 5 public rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT Realtime mini
- Not ranked
- Kimi K2.6
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT Realtime mini
- Not ranked
- Kimi K2.6
- 83.5
- #57/157
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT Realtime mini
- Not ranked
- Kimi K2.6
- 76.8
- #4/12
- Basis
- Provisional lane · 0 vs 4 weighted rows
- Reading
- Not comparable
| Category | GPT Realtime mini | Kimi K2.6 | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | 44.5Supported · #67/135 | Not comparableBenchAlign v5.8 lane · 0 vs 12 public rows | Not comparable |
| Coding | Not ranked | 42.9Supported · #63/156 | Not comparableBenchAlign v5.8 lane · 0 vs 10 public rows | Not comparable |
| Reasoning | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 73.5#30/58 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Knowledge | Not ranked | 58.8Supported · #52/179 | Not comparableBenchAlign v5.8 lane · 0 vs 5 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 83.5#57/157 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 76.8#4/12 | Not comparableProvisional lane · 0 vs 4 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
- GPT Realtime mini
- $0.0018
- Fits in one request
- Kimi K2.6
- $0.00295
- Fits in one request
GPT Realtime mini has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT Realtime mini
- $0.0372
- Does not fit in one request
- Kimi K2.6
- $0.0595
- Fits in one request
GPT Realtime mini does not fit this workload in one request.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT Realtime mini
- $0.048
- Does not fit in one request
- Kimi K2.6
- $0.091
- Fits in one request
GPT Realtime mini does not fit this workload in one request.
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.
SpecificationGPT Realtime miniKimi K2.6
Context window
Maximum documented context; output-token limits may be lower.
GPT Realtime mini
Kimi K2.6
256K
API model ID
GPT Realtime mini
gpt-realtime-mini
OpenAI model documentationKimi K2.6
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT Realtime mini
$0.06 per 1M cached input tokens
OpenAI model documentationKimi K2.6
$0.16 per 1M cached input tokens
Documented inputs
GPT Realtime mini
Not sourced
Kimi K2.6
Not sourced
Documented outputs
GPT Realtime mini
Not sourced
Kimi K2.6
Not sourced
Provider availability
GPT Realtime mini
Not sourced
Kimi K2.6
Not sourced
Reasoning profile
GPT Realtime mini
Non-Reasoning
Kimi K2.6
Reasoning
Weight access
GPT Realtime mini
Proprietary
Kimi K2.6
Open Weight
License
GPT Realtime mini
Proprietary
Kimi K2.6
Open Weight
Release date
GPT Realtime mini
2025-10-06
Kimi K2.6
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
- Repository review: $0.0372 vs $0.0595. Cache-heavy agent loop: $0.048 vs $0.091.
- Context tradeoff
- Kimi K2.6 has the larger documented window (256K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT Realtime mini or Kimi K2.6?
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, GPT Realtime mini or Kimi K2.6?
GPT Realtime mini is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT Realtime mini or Kimi K2.6?
GPT Realtime mini is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT Realtime mini or Kimi K2.6?
For the stated presets, chat costs $0.0018 on GPT Realtime mini and $0.00295 on Kimi K2.6; repository review costs $0.0372 and $0.0595; the cache-heavy agent loop costs $0.048 and $0.091. GPT Realtime mini does not fit this workload in one request.
Which has the larger context window, GPT Realtime mini or Kimi K2.6?
Kimi K2.6 has the larger documented context window: 256K, compared with 32K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
GPT Realtime mini
API / mo$2,250
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Kimi K2.6
API / mo$3,713
Self-host / mo$27,962
Break-even500M/day
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence37 rows
Agentic
Terminal-Bench 2.0
GPT Realtime mini—Kimi K2.666.7%SourceNot directly comparable
BrowseComp
GPT Realtime mini—Kimi K2.683.2%SourceNot directly comparable
OSWorld-Verified
GPT Realtime mini—Kimi K2.673.1%SourceNot directly comparable
Toolathlon
GPT Realtime mini—Kimi K2.650%SourceNot directly comparable
MCP Atlas
GPT Realtime mini—Kimi K2.655.9%SourceNot directly comparable
Claw-Eval
GPT Realtime mini—Kimi K2.662.3%SourceNot directly comparable
DeepSearchQA
GPT Realtime mini—Kimi K2.692.5%SourceNot directly comparable
WideResearch
GPT Realtime mini—Kimi K2.680.8%SourceNot directly comparable
Gert Labs
GPT Realtime mini—Kimi K2.656.82%SourceNot directly comparable
ResearchClawBench
GPT Realtime mini—Kimi K2.618.0%SourceNot directly comparable
OSWorld 2.0
GPT Realtime mini—Kimi K2.64.6%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
GPT Realtime mini—Kimi K2.653.6%SourceNot directly comparable
Coding
SWE-bench Verified
GPT Realtime mini—Kimi K2.680.2%SourceNot directly comparable
LiveCodeBench v6
GPT Realtime mini—Kimi K2.689.6%SourceNot directly comparable
SWE-bench Pro
GPT Realtime mini—Kimi K2.658.6%SourceNot directly comparable
SWE Multilingual
GPT Realtime mini—Kimi K2.676.7%SourceNot directly comparable
SciCode
GPT Realtime mini—Kimi K2.652.2%SourceNot directly comparable
Terminal-Bench 2.0
GPT Realtime mini—Kimi K2.666.7%SourceNot directly comparable
Vibe Code Bench
GPT Realtime mini—Kimi K2.637.89%SourceNot directly comparable
cursorBench31
GPT Realtime mini—Kimi K2.647.6%SourceNot directly comparable
LiveCodeBench (Vals)
GPT Realtime mini—Kimi K2.686.8%SourceNot directly comparable
SWE-bench (Vals)
GPT Realtime mini—Kimi K2.676.2%SourceNot directly comparable
Multimodal
MMMU-Pro
GPT Realtime mini—Kimi K2.679.4%SourceNot directly comparable
MMMU-Pro w/ Python
GPT Realtime mini—Kimi K2.680.1%SourceNot directly comparable
CharXiv
GPT Realtime mini—Kimi K2.680.4%SourceNot directly comparable
MathVision
GPT Realtime mini—Kimi K2.687.4%SourceNot directly comparable
V*
GPT Realtime mini—Kimi K2.696.9%SourceNot directly comparable
Knowledge
GPQA
GPT Realtime mini—Kimi K2.690.5%SourceNot directly comparable
GPQA-D
GPT Realtime mini—Kimi K2.690.5%SourceNot directly comparable
HLE
GPT Realtime mini—Kimi K2.634.7%SourceNot directly comparable
GPQA Diamond (Vals)
GPT Realtime mini—Kimi K2.689.1%SourceNot directly comparable
MMLU-Pro (Vals)
GPT Realtime mini—Kimi K2.687.6%SourceNot directly comparable
Math
AIME26
GPT Realtime mini—Kimi K2.696.4%SourceNot directly comparable
HMMT Feb 2026
GPT Realtime mini—Kimi K2.692.7%SourceNot directly comparable
MMAnswerBench
GPT Realtime mini—Kimi K2.686.0%SourceNot directly comparable
FrontierMath v2 (Tiers 1-3)
GPT Realtime mini—Kimi K2.657.193%SourceNot directly comparable
FrontierMath v2 (Tier 4)
GPT Realtime mini—Kimi K2.625.641%SourceNot directly comparable
37 public results · 0 shared
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Last updated October 29, 2026