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Cheapest LLM APIs 2026: Low-Cost AI Models Ranked by Workload

Discover the cheapest LLM APIs in 2026: blended input/output cost, batch discounts, and prompt caching. Compare budget AI models for startups and agencies in the US, Canada, and Australia.

Lowest estimated monthly API cost for the same workload in 2026

The cheapest tab ranks models by estimated spend for your exact monthly requests and token pattern, including batch and cached pricing only when our database confirms eligibility. Founders and agencies in the United States, Canada, and Australia use it to protect margins on high-volume chat, summarization, and RAG pipelines without guessing list prices from blog posts.

Est. monthlyROI scoreCodingReasoningSpeedMathContextOverallOpen-weight

Workload & pricing toggles

Workload presets

Same three scenarios as the main AI API calculator: moderate traffic, large RAG-style context, or per-request max tokens with a lower request count.

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Off — no image fees in cost estimates for vision-capable models.

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Enable to get 50% off input tokens where cached rates apply

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Deep Reasoning / Thinking Mode

Model hidden reasoning / extended thinking charged like output tokens when enabled.

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Batch Pricing

Enable for 50% off input & output where batch/async pricing applies

OffOn
≈ $100.00/mo
8K
1K—1.0M
≈ $100.00/mo
2K
100—500K
≈ $200.00 total
5K
10—100K

Cached / batch est. monthly values only change after the pipeline sets supports_caching or supports_batch in Supabase. The toggles here narrow the table to models whose catalog or provider typically supports those modes.

Magic quadrant (top 15)

X: est. monthly · Y: Cheapest (est. monthly) · Dot: provider color · Hover for rank, model & details

Full leaderboard

Showing 48 of 444 models.

PickModelEst. monthlyROI scoreCodingReasoningSpeedMathContextOverall
Ox Alpha
Free
79
90
88
85
85
1.0M
88Primary source: Web digest (102.5 tok/s). Lacking exact SWE-bench or GPQA percentages for Ox Alpha, capabilities are inferred for a flagship reasoning tier. Speed mapped to 85 based on the cited high throughput.
Elephant
Free
77
90
83
70
88
262K
86
Google: Lyria 3 Clip Preview
Free
31
0
5
50
0
1.0M
3Lyria 3 is a specialized music generation model lacking standard LLM benchmarks (SWE-bench, GPQA). Assigned 0 for coding/logic/math. Speed mapped to 50 from 38 tok/s. Multimodal scored 85 for native image-to-audio generation.
Owl Alpha
Free
66
65
68
85
60
1.0M
65No exact scores for Owl Alpha; inferred as a lightweight reasoning model ('fewer parameters', 'designed for speed'). Mapped to mid-tier 0-100 scale (Coding 65, Logic 65) reflecting its agentic focus but smaller size.
Auto Router (Beta)
VARIABLE
77
85
85
70
85
2.0M
85Primary source: OpenRouter docs (no raw scores). Inferred frontier-level capabilities (~85) across coding, logic, instruction, and math based on task-aware routing to popular models. Speed estimated at 70. Reasoning explicitly omitted for dynamic routers.
Google: Lyria 3 Pro Preview
Free
67
70
70
55
60
1.0M
68Evidence notes Lyria 3 Pro scores well on SWE-bench and MMLU without exact figures. Mapped to 70s for Pro tier. Speed is 39.5 tok/s (55). Multimodal audio generation from images supported; default Pro vision price applied.
OpenRouter: Fusion
VARIABLE
77
85
85
40
85
1.0M
85No explicit Fusion scores provided. Inferred as a heavyweight ensemble ('panel of expert models'), mapping to ~85 across coding (SWE-bench) and logic (GPQA). Speed is rated lower (40) due to multi-model deliberation and web search overhead.
Auto Router
VARIABLE
80
90
90
70
90
2.0M
90Auto Router optimizes across models for best output. Evidence cites top models reaching 92.3% MMLU. Mapped to 90 across logic, coding, and math to reflect frontier routing capabilities. Vision price defaulted to $0.007 per tier guidelines.
Body Builder (beta)
VARIABLE
54
45
43
90
40
128K
43Lacking specific benchmarks, inferred from cited 1B-scale Fast-dLLM v2 (43.5 avg across HumanEval, GPQA, GSM8K). Mapped to 40-45 for coding, logic, math. Speed rated 90 for lightweight specialized API tool.
Free Models Router
Free
55
45
48
90
45
200K
46No specific benchmarks cited for the Free Router. Inferred lightweight tier scores (Coding/Logic ~45) based on typical free 8B-class models. Speed rated high (90). Vision price is $0 as the endpoint is free.
Pareto Code Router
VARIABLE
77
88
85
70
85
2.0M
86OpenRouter docs state this is a router defaulting to High tier coding models based on Artificial Analysis percentiles. Lacking specific raw benchmarks, scores are mapped to ~85 reflecting flagship-level routed performance. Text-only inputs confirmed.
inclusionAI: Ling-2.6-flash$0.7082
65
68
90
65
262K
66Evidence cites GPQA, AIME, and LiveCodeBench without raw scores. Mapped to ~65 for coding/logic based on claimed ~40B dense equivalence. Speed scored 90 due to 200+ tokens/s. Flash tier adjustment applied.
Mistral: Mistral Nemo$1.0665
45
50
90
35
131K
45GPQA 5.37% maps to Logic 35. IFEval 63.8% maps to Instruction 65. MATH Lvl 5 is 12.69%. As a 12B lightweight model, it scores lower on coding/logic than flagships but achieves high speed (90).
inclusionAI: Ling 3.0 Flash$1.4766
35
63
95
55
262K
54Evidence lacks exact Ling-3.0-flash scores, so inferred from Ling-2.6-flash (GPQA 59.3%, Coding 25.3) and Flash tier traits. Mapped GPQA to Logic 60, Coding to 35. Speed set to 95 reflecting 94 tok/s lightweight MoE architecture.
IBM: Granite 4.0 Micro$1.8065
45
58
85
65
131K
56Based on GPQA 32.15% (Logic ~35) and HumanEval 81.00% (Coding ~45). IFEval averages 84.32% (Instruction ~80). As a 3B 'Micro' tier model, scores reflect lower reasoning capacity compared to flagships, but strong instruction following.
Nex AGI: Nex-N2-Mini$2.0068
45
70
90
70
262K
64No exact N2-Mini benchmarks provided; inferred from N1 8B/32B SWE-bench Verified (20.3-50.5) and N2 Pro MMLU (89.2). Mapped coding to 45 and logic to 70, adjusting downward for the Mini tier. Speed rated high (90).
Sao10K: Llama 3 8B Lunaris$2.1062
45
63
90
45
8K
54Evidence lacks specific benchmark scores. Inferred from Llama 3 8B lightweight tier: coding and math mapped to ~45, logic ~60. Speed rated high (90) due to small 8B parameter size.
Upstage: Solar Pro 4$2.4072
70
78
60
75
524K
75Evidence lacks exact Solar Pro 4 scores but cites SWE-bench, GPQA, and MMLU for the Pro series. Mapped as a flagship MoE (102B+ class): Coding 70, Logic 75. Native reasoning confirmed via 'reasoning_effort' parameter.
LiquidAI: LFM2-24B-A2B$2.4060
71
44
97
55
128K
54Benchable.ai cites Coding 71%, Reasoning 50%, Instruction 38%, and Speed at 97th percentile. Mapped directly to 0-100 scale. As a lightweight 2B active MoE, it prioritizes speed over flagship-level logic and coding.
OpenAI: gpt-oss-20b$2.5075
70
80
90
95
131K
81GPQA 71.5% and MMLU 85.3% map to Logic 80. AIME 2025 98.7 maps to Math 95. As a 21B lightweight MoE, Speed is 90. Coding inferred at 70 due to lack of explicit SWE-bench.
Qwen: Qwen3.7 Flash$2.5064
55
63
90
65
1.0M
61No exact SWE-bench or GPQA scores provided for Qwen 3.7 Flash. Inferred from Flash lightweight tier: Speed mapped high (90), Coding/Logic mapped lower (55). Multimodal supported natively.
Qwen: Qwen-Turbo$2.6057
50
50
50
50
131K
50
Mistral: Mistral Small 3$2.8070
75
75
90
75
33K
75HumanEval 88.41% maps to coding 75. GPQA Diamond 45.96% maps to logic 70. As a 24B 'Small' tier model, it scores lower than flagships but achieves high speed (90).
Meta: Llama 3.1 8B Instruct$2.8051
45
48
90
25
131K
41MMLU 66.7%, GPQA 8.72%, MATH 15.56%, IFEval 49.22%. As an 8B lightweight tier, scores map to low/moderate logic (45) and math (25). Speed is rated high (90) due to its small, efficient architecture.
Amazon: Nova Micro 1.0$2.8066
68
63
95
75
128K
67HumanEval 81.1% (Coding 68), GPQA 40% (Logic 45), IFEval 87.2% (Instruction 80), GSM8K 92.3% (Math 75). As a 'Micro' tier model, speed is rated very high (95) while coding and logic reflect its lightweight, text-only nature.
Cohere: Command R7B (12-2024)$3.0052
35
48
90
40
128K
43Evidence explicitly states no benchmark data is available, requiring a conservative rating. As a 7B lightweight model, capabilities are estimated lower than flagships, while speed is rated high (90) due to its small, fast architecture.
Google: Gemma 3 4B$3.0063
45
68
95
75
131K
64Based on IFEval (90.2%) mapped to 90 Instruction, MATH (75.6%) to 75 Math, and MMLU-Pro (43.6%) to 45 Logic. As a 4B lightweight tier, Coding (MBPP 63.2%) maps to 45, while Speed is rated 95.
MythoMax 13B$3.0049
35
45
85
30
8K
39Evidence cites SWE-bench and HumanEval without exact scores. As a 13B Llama-2 fine-tune, capabilities are inferred as lightweight tier. Assigned ~35-40 for coding/logic, with high speed (85) reflecting its small parameter count and fast inference claims.
IBM: Granite 4.1 8B$3.0064
68
64
90
65
131K
65HumanEval >89.7% (coding ~68), MMLU >66% (logic ~58), IFEval >74.8% (instruction ~70) based on 3.3 baseline. Lightweight 8B tier adjustments applied for high speed and scaled capabilities.
Meta: Llama 3.2 1B Instruct$3.0945
25
35
95
30
60K
31MMLU 49.3%, GSM8K 44.4%, MATH 30.6%. As a 1B lightweight model, Logic (35) and Math (30) reflect sub-50% benchmarks. Coding (25) based on 0.6 index. Speed (95) is maximized for this ultra-small tier.
Inception: Mercury 2.5$3.1071
75
75
100
88
260K
78Based on GPQA (73.6-77%), HumanEval (85%), and IFBench (71.3%), mapped to mid-tier 70-80s. Speed is exceptional at ~1,107 tps (100). As a speed-optimized dLLM, it trades flagship coding for extreme throughput and tunable reasoning.
Inception: Mercury 2.5 Preview$3.1052
45
45
100
45
260K
45Fast-dLLM v2 averages 43.5 across HumanEval, GPQA, and MMLU, mapping to ~45 for coding/logic. As a 1B-scale lightweight model, it prioritizes speed (>1,000 tok/s, scoring 100) over flagship-level reasoning.
OpenAI: gpt-oss-120b$3.1869
70
80
95
75
131K
76HumanEval 71% maps to 70 coding. MMLU 66-90% maps to 80 logic. GSM8K 75% maps to 75 math. 500 tok/s throughput maps to 95 speed. Native reasoning supported via OpenRouter reasoning parameter.
NVIDIA: Nemotron Nano 9B V2$3.2066
70
63
90
85
131K
70Evidence shows GPQA at 64.0% (Logic ~65) and LiveCodeBench at 72.4% (Coding ~70). MATH-500 is 97.8% (Math ~85). As a 9B lightweight reasoning model, it achieves high speed (115 tok/s, Speed ~90) but lacks multimodal support.
Arcee AI: Trinity Mini$3.3075
82
89
90
88
131K
87GPQA Diamond at 92.1% maps to 92 Logic. AIME 2025 at 58.6% maps to 88 Math. LM Market Cap coding score of 82 maps to 82 Coding. As a 'Mini' tier, speed is rated high (90).
Google: Gemma 3 12B$3.5067
70
70
88
85
131K
74HumanEval 85.4% (Coding ~70), GPQA 40.9% (Logic ~55), IFEval 88.9% (Instruction ~85), MATH 83.8% (Math ~85). As a 12B lightweight tier, scores reflect strong math/instruction but moderate logic/coding compared to flagships.
Tencent: Hy-MT2-1.8B$3.5339
20
25
85
20
8K
23Evidence lacks specific benchmark percentages for HY-MT2-1.8B, noting only 18 tokens/sec on M4. As a 1.8B lightweight translation model, coding, logic, and math are inferred low (~20-30), while speed is rated high (85) for its compact size.
Poolside: Laguna XS 2.1$3.6068
88
73
92
70
262K
76SWE-bench Verified at 68.2% maps to 88 Coding. As an XS (33B/3B active) lightweight tier, Speed is high (92). Logic (70) and Math (70) are inferred due to lack of GPQA/MATH evidence.
Google: Gemma 3n 4B$3.6061
60
63
90
65
33K
63ARC-E 81.6% and HellaSwag 78.6% map to ~55 Logic. Outperforms Gemma 2 9B on HumanEval, mapping to ~60 Coding. As a 4B lightweight model, Speed is rated high (~90) while reasoning is scaled down.
DeepSeek V4 Flash Latest$3.6074
85
88
92
88
1.3M
87SWE-bench Verified 79.0% maps to 85 coding. GPQA Diamond 88.1 maps to 90 logic. As a Flash tier model, speed is rated high (92), though its native reasoning modes elevate logic scores near flagship levels.
Qwen: Qwen3 30B A3B Instruct 2507$3.8664
68
70
85
70
262K
70Evidence cites HumanEval, GPQA, and IFEval testing but lacks exact scores. As a 30B (3.3B active) lightweight MoE, scores are inferred: Coding ~68, Logic ~65. Speed is high (77 tok/s), mapped to 85.
NVIDIA: Nemotron 3 Nano 30B A3B$4.0064
60
68
90
85
262K
70Evidence lacks exact percentages but notes AIME 2025 wins over DeepSeek-V3.1 (Math: 85) and GPQA/SWE-Bench Verified losses to GLM-4.7 (Logic: 70, Coding: 60). As a 3B-active Nano tier, speed is heavily weighted (90).
inclusionAI: Ling 3.0 Flash Fin$4.2053
35
60
85
55
262K
52Used Ling 2.6 Flash proxy scores: GPQA 59.3% (Logic 59) and Coding 25.3 (Coding 35). As a Flash-tier lightweight MoE, capability scores are kept lower than flagships, while Speed is rated high (85).
Microsoft: Phi 4$4.2062
65
68
85
70
16K
68Evidence lacks exact benchmark scores for base Phi-4 14B. Inferred mid-tier scores (Coding 65, Logic 65) based on its 14B parameter size. Multimodal and native reasoning are false as those belong to separate Phi-4-Vision and Phi-4-Reasoning variants.
DeepSeek: DeepSeek V4 Flash 0731$4.4072
88
87
92
82
1.3M
86SWE-bench Verified (79.0%) and GPQA Diamond (88.1%) map to 88 coding and 89 logic. As a Flash tier (13B active), speed is rated high (92), though native reasoning modes boost its benchmark scores significantly.
Amazon: Nova Lite 1.0$4.8064
75
73
90
75
300K
74HumanEval 85.4% (Coding ~75), GPQA 42% (Logic ~60), IFEval 89.7% (Instruction ~85), MATH 73.3% (Math ~75). As a 'Lite' tier model, it prioritizes speed (~90) over flagship reasoning, reflecting lower GPQA and coding capabilities.
DeepSeek: DeepSeek V4 Flash 0423$4.8667
68
79
90
85
1.0M
78V4 flagship claims 80%+ SWE-bench; Flash tier (13B active) lacks explicit scores but is inferred ~68 for coding. Logic and Math scaled down for Flash efficiency. Speed rated 90 for fast inference design.
Mistral: Mistral Small 3.2 24B$5.0062
75
70
88
70
131K
71GPQA Diamond 46.13% maps to 65 logic; HumanEval+ 92.90% maps to 75 coding; MATH 69.42% maps to 70 math. As a 24B Small tier model, speed is rated high (88) reflecting its lightweight, cost-optimized architecture.

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To achieve the absolute lowest cost per million tokens, modern architectures combine two features: Batch APIs (which typically offer a 50% discount for 24-hour turnaround) and Prompt Caching (which discounts large, static system prompts by up to 90%). Filter this leaderboard by 'Batch pricing' to identify vendors that support these aggressive cost-reduction features.

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