Long documents
Prompts that approach the documented context limit
Ling 3.1 Flash
Ling 3.1 Flash has the larger documented context window.
Confidence: documented
GPT-5 mini vs Ling 3.1 Flash
Updated November 13, 2026. Rank says Ling 3.1 Flash is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Share or export
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.
- API price per 1M tokens
GPT-5 mini: $0.25 input / $2 output
Ling 3.1 Flash: No comparable API price
- Context window
GPT-5 mini: 128K
Ling 3.1 Flash: 262K
- Released
GPT-5 mini: August 7, 2025
Ling 3.1 Flash: September 29, 2026
- Shared results
- 0
- GPT-5 mini only
- 1
- Ling 3.1 Flash only
- 17
- Like-for-like categories
- 0 / 8
Supported: GPT-5 mini · Estimated: Ling 3.1 Flash. 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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GPT-5 mini and Ling 3.1 Flash are 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
GPT-5 mini and Ling 3.1 Flash are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedChat 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-ratesCache-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-5 mini does not fit this workload in one request. GPT-5 mini has no published cached-input rate, so cached tokens use its listed input rate. Ling 3.1 Flash has no comparable published API token rate.
Confidence: rate-fallbackRepository 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
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.
28.1GPT-5 mini56.8Ling 3.1 Flash
Directional only · BenchAlign v5.8
Ling 3.1 Flash 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
Or describe your own job
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.
We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 GPT-5 mini only
17 Ling 3.1 Flash only
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, reasoning, multimodal, 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- GPT-5 mini
- 30.3
- Estimated · #103/135
- Ling 3.1 Flash
- 61.4
- Estimated · #30/135
- Basis
- BenchAlign v5.8 lane · 0 vs 10 public rows
- Reading
- Directional only
Coding
Directional only- GPT-5 mini
- 28.1
- Estimated · #118/162
- Ling 3.1 Flash
- 56.8
- Estimated · #29/162
- Basis
- BenchAlign v5.8 lane · 1 vs 5 public rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5 mini
- Not ranked
- Ling 3.1 Flash
- 61.4
- Estimated · #28/160
- Basis
- BenchAlign v5.8 lane · 0 vs 0 public rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5 mini
- Not ranked
- Ling 3.1 Flash
- Not ranked
- Basis
- BenchAlign v5.8 lane · 0 vs 0 public rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5 mini
- Not ranked
- Ling 3.1 Flash
- Not ranked
- Basis
- BenchAlign v5.8 lane · 0 vs 2 public rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5 mini
- Not ranked
- Ling 3.1 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5 mini
- Not ranked
- Ling 3.1 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5 mini
- Not ranked
- Ling 3.1 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5 mini | Ling 3.1 Flash | Basis | Reading |
|---|---|---|---|---|
| Agentic | 30.3Estimated · #103/135 | 61.4Estimated · #30/135 | Directional onlyBenchAlign v5.8 lane · 0 vs 10 public rows | Directional only |
| Coding | 28.1Estimated · #118/162 | 56.8Estimated · #29/162 | Directional onlyBenchAlign v5.8 lane · 1 vs 5 public rows | Directional only |
| Reasoning | Not ranked | 61.4Estimated · #28/160 | Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows | Not comparable |
| Knowledge | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 2 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, reasoning, multimodal, 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.
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-5 mini
- $0.00125
- Fits in one request
- Ling 3.1 Flash
- API rate not published
- Fits in one request
Ling 3.1 Flash has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GPT-5 mini
- $0.0185
- Fits in one request
- Ling 3.1 Flash
- API rate not published
- Fits in one request
Ling 3.1 Flash has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-5 mini
- $0.075
- Does not fit in one request
- Cached input priced at the published list-input rate
- Ling 3.1 Flash
- API rate not published
- Fits in one request
- Cached-input rate unavailable
GPT-5 mini does not fit this workload in one request. GPT-5 mini has no published cached-input rate, so cached tokens use its listed input rate. Ling 3.1 Flash has no comparable published API token rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Price against score, among all priced models
Overall score against output price per 1M tokens, one model per family. Pricing as of November 12, 2026.
GPT-5 mini
- 9 cheaper models score at least as high as GPT-5 mini. The nearest is Quasar 438B (score 49.1, $1.80 per 1M output tokens).
- Ling 3.1 Flash has no public API price, so it is not plotted.
Gray dots are the other priced models. The dark step line is the efficiency frontier: for a model under it, another model costs no more and scores at least as high.
See GPT-5 mini on the full price vs performance chartSpecification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5 miniLing 3.1 Flash
Context window
Maximum documented context; output-token limits may be lower.
GPT-5 mini
128K
Ling 3.1 Flash
API model ID
GPT-5 mini
Not sourced
Ling 3.1 Flash
inclusionai/ling-3.1-flash
Vercel AI Gateway Ling 3.1 Flash routeCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5 mini
Not published
Ling 3.1 Flash
No comparable hosted API rate
Vercel AI Gateway availabilityDocumented inputs
GPT-5 mini
text, image
OpenAI model page for gpt-5-miniLing 3.1 Flash
Documented outputs
GPT-5 mini
Ling 3.1 Flash
Provider availability
GPT-5 mini
Not sourced
Ling 3.1 Flash
Generally Available · Hugging Face weights, SGLang, vLLM, Vercel AI Gateway API via Novita
Vercel AI Gateway Ling 3.1 Flash routeReasoning profile
GPT-5 mini
Reasoning
Ling 3.1 Flash
Reasoning
Weight access
GPT-5 mini
Proprietary
Ling 3.1 Flash
Open Weight
License
GPT-5 mini
Proprietary
Ling 3.1 Flash
Open Weight
Release date
GPT-5 mini
2025-08-07
Ling 3.1 Flash
2026-09-29
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
- Ling 3.1 Flash has the larger documented window (262K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-5 mini or Ling 3.1 Flash?
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-5 mini or Ling 3.1 Flash?
Ling 3.1 Flash scores higher for coding on the public lane, 56.8 to 28.1. GPT-5 mini and Ling 3.1 Flash are 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, GPT-5 mini or Ling 3.1 Flash?
Ling 3.1 Flash scores higher for agentic tasks on the public lane, 61.4 to 30.3. GPT-5 mini and Ling 3.1 Flash 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, GPT-5 mini or Ling 3.1 Flash?
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, GPT-5 mini or Ling 3.1 Flash?
Ling 3.1 Flash has the larger documented context window: 262K, compared with 128K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
GPT-5 mini
API / mo$1,688
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Ling 3.1 Flash
API / mo (Novita AI)$900
Self-host / mo$27,962
Break-evenNot reached at 70% load
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence18 rows
Agentic
skillsBench
GPT-5 mini—Ling 3.1 Flash68.7%SourceNot directly comparable
AutomationBench
GPT-5 mini—Ling 3.1 Flash52.5%SourceNot directly comparable
CyberGym
GPT-5 mini—Ling 3.1 Flash87.9%SourceNot directly comparable
Finance Agent v2
GPT-5 mini—Ling 3.1 Flash57.9%SourceNot directly comparable
DRACO
GPT-5 mini—Ling 3.1 Flash85.5%SourceNot directly comparable
Terminal-Bench 2.1
GPT-5 mini—Ling 3.1 Flash81.2%SourceNot directly comparable
τ³-bench results
GPT-5 mini—Ling 3.1 Flash47.8%SourceNot directly comparable
MCP Atlas
GPT-5 mini—Ling 3.1 Flash83%SourceNot directly comparable
BrowseComp
GPT-5 mini—Ling 3.1 Flash91.7%SourceNot directly comparable
WideResearch
GPT-5 mini—Ling 3.1 Flash83.3%SourceNot directly comparable
Coding
Vibe Code Bench
GPT-5 mini14.17%SourceLing 3.1 Flash—Not directly comparable
terminalBench4
GPT-5 mini—Ling 3.1 Flash46.0%SourceNot directly comparable
SWE-Atlas Codebase QnA
GPT-5 mini—Ling 3.1 Flash64.5%SourceNot directly comparable
SWE Multilingual
GPT-5 mini—Ling 3.1 Flash88.5%SourceNot directly comparable
SWE-bench Pro
GPT-5 mini—Ling 3.1 Flash68.7%SourceNot directly comparable
DeepSWE
GPT-5 mini—Ling 3.1 Flash59.7%SourceNot directly comparable
Knowledge
18 public results · 0 shared
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Last updated November 13, 2026