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Best LLM ROI 2026: Value vs. API Cost for AI Teams

See the best LLM ROI in 2026: value density from benchmarks divided by estimated API spend. Compare cost-effective AI models for product teams in the US, Canada, and Australia.

Value-for-money LLM rankings you can explain to finance in 2026

ROI mode highlights models that punch above their price: strong benchmark signals relative to estimated monthly API bills. Revenue teams and consultancies in the United States, Canada, and Australia use it to defend model choices in proposals—especially when clients ask why you did not default to the most famous flagship.

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.

Include Vision / Image Processing

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

Turn On to include image fees.

OffOn

Use Cached Pricing

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

OffOn

Deep Reasoning / Thinking Mode

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

OffOn

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: ROI / Value · Dot: provider color · Hover for rank, model & details

Full leaderboard

Showing 48 of 513 models.

PickModelEst. monthlyROI scoreCodingReasoningSpeedMathContextOverall
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.
Space Bunny Alpha
Free
80
92
89
90
94
1.0M
91SWE-bench Verified at 65.4% maps to 92 coding. MMLU-Pro at 86.2% maps to 90 logic. MATH-500 at 92.8% maps to 94 math. High 118.5 TPS maps to 90 speed. No tier adjustment needed for this stealth flagship.
NVIDIA: Switchyard
VARIABLE
80
90
90
70
95
1.0M
91Switchyard is a router. Evidence cites top models hitting HumanEval 95-99%, MMLU 88-94%, and GPQA 65-74%, mapped to ~90s for coding and logic. NVIDIA NIM evidence shows 'detailed thinking' reasoning support. No vision or caching cited.
Z.ai: GLM Flash Latest$2.3280
85
87
95
95
1.0M
89HumanEval 94.2%, GPQA 85.7%, IFEval 88.0%, AIME 95.7%. Mapped directly to 0-100 scale. As a Flash tier model, speed is rated high (95), while coding/logic reflect strong but lightweight capabilities compared to flagship GLM-5.
OpenAI: gpt-oss-20b$1.6280
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.
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.
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
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.
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.
Switchpoint Router
VARIABLE
77
85
85
30
85
131K
85No raw benchmarks provided for the router; inferred frontier-level scores as it routes to top models (evidence cites Opus 4.1 at 74.5% SWE-bench Verified). Speed is scored low due to 2.5-6.0 tok/s reported throughput.
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.
Nex AGI: Nex-N2.5-Mini$2.0077
74
86
90
75
262K
80SWE-bench Verified at 74.4 maps to 74 coding. GPQA Diamond at 82.6 maps to 82 logic. IFEval at 89.1 maps to 89 instruction. As a 'Mini' tier, speed is rated high (90), with strong multimodal scores (OmniDoc 89.7).
DeepSeek: DeepSeek V4 Flash 0731$3.1175
88
87
92
82
1.0M
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.
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).
DeepSeek: DeepSeek V4 Flash Latest$3.4575
85
88
92
88
1.0M
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: Qwen3.5-Flash$5.2074
88
92
95
95
1.0M
92SWE-bench Verified at 69.2% maps to 88 coding. GPQA Diamond at 84.2% maps to 92 logic. IFEval 91.9% maps to 92 instruction. As a Flash tier, speed is 95, though its reasoning capabilities rival flagship models.
Xiaomi: MiMo-V2-Flash$7.0071
92
88
90
95
262K
91SWE-bench Verified 73.4% maps to 92 coding. GPQA 83.7% maps to 90 logic. AIME 94.1% maps to 95 math. Despite the 'Flash' name, its 309B MoE architecture and native reasoning deliver flagship-level scores.
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.
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).
inclusionAI: Ling 3.1 Flash$5.2570
85
80
95
95
262K
85SWE-bench Pro (56.6%) maps to 85 coding. AIME (93.2) maps to 95 math. As a Flash-tier model, speed is heavily weighted (95), while logic and instruction are inferred lower than flagship counterparts.
Qwen: Qwen3 235B A22B Instruct 2507$9.1070
92
93
70
92
262K
92SWE-bench Verified 55.6% maps to 92 coding. GPQA 77.5% maps to 95 logic. IFEval 93.3% maps to 90 instruction. Flagship 235B MoE tier yields 70 speed.
PrismML: Ternary Bonsai 2 27B$8.0069
89
87
45
96
262K
90HumanEval+ 95.12% and MMLU-Redux 89.09% map to coding (89) and logic (85). MATH-500 98.80% yields exceptional math (96). As a 27B reasoning model, speed is lower (45) due to default xhigh thinking effort.
Nex AGI: Nex-N2.5-Pro$5.5069
88
85
72
80
262K
85SWE-Bench Pro 61.2% maps to 88 coding. OmniDoc 92.2% supports 82 multimodal. Logic and math inferred from Pro tier status. Native reasoning and caching confirmed via provider docs.
NVIDIA: Nemotron 3.5 Super VL$11.0069
95
91
95
93
1.0M
93SWE-bench Verified at 60.47% maps to 95 coding. GPQA at 79.23% maps to 92 logic. HMMT math is 93.67% (93). Speed is exceptional at 449 tok/s (95). Native reasoning is supported via enable_thinking.
inclusionAI: Ling-2.6-1T$9.2569
92
90
75
92
262K
91Evidence claims state-of-the-art on SWE-bench Verified and AIME26 without raw scores. As a 1T flagship, mapped coding and math to 92. Logic and instruction mapped to 90. Speed mapped to 75 due to 'fast execution' claims.
inclusionAI: Ling 3.0 Flash VL$4.2069
85
73
95
90
131K
80SWE-Bench Pro at 56.6% maps to 85 coding. MathArena AIME 93.2 maps to 90 math. Speed is 95 (138.8 t/s). As a Flash-tier MoE, logic (HLE 22.7) and instruction are scaled to 70-75.
Nex AGI: DeepSeek V3.1 Nex N1$10.4069
96
90
70
90
131K
92Flagship tier. SWE-bench Verified at 70.6 maps to 96 coding. BFCL v4 at 65.3 maps to 90 instruction. Logic and math inferred high (90) from flagship status. Speed set to 70 for heavy MoE.
DeepSeek: DeepSeek V3.2 Speciale$15.7969
95
97
45
96
164K
96GPQA 87.1% maps to 98 logic. LiveCodeBench 89.6% and Aider 88.0% map to 95 coding. HMMT 99.0% maps to 96 math. Flagship reasoning model with native thinking tokens; speed scored 45 due to extended reasoning overhead.
StepFun: Step 3.5 Flash$7.0069
88
83
95
95
262K
87SWE-bench Verified 74.4% maps to 88 coding; AIME 99.8% maps to 95 math. Despite Flash tier, explicit evidence shows frontier-level SWE-bench Verified, elevating coding score. Speed is 95 (143 tok/s).
Gemma 4 26B A4B$4.9569
77
81
90
88
262K
82GPQA Diamond 82.3% maps to 82 logic. LiveCodeBench 77.1% maps to 77 coding. AIME 88.3% maps to 88 math. As a 3.8B active MoE, speed is rated 90. MMMU Pro 73.8% yields 78 multimodal.
Inception: Mercury 3$7.5068
88
88
100
85
1.0M
87Cited HumanEval 90.0 (Coder Small), plus GPQA Diamond, IFBench, and SWE-Bench Atlas wins over GPT-6 Luna. Mapped to Coding 88, Logic 88, Instruction 88. Speed set to 100 due to unprecedented 1,068 tokens/sec throughput.
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.
Qwen: Qwen3 32B$6.0068
78
85
70
88
131K
84No exact percentages cited; evidence notes Qwen3 32B outperforms Qwen2.5 72B on GPQA and IFEval. Mapped Logic/Instruction to 85. Speed mapped to 70 based on 57 tok/s. Native reasoning supported via dual-mode architecture.
Meta: Llama 3.3 70B Instruct$7.2068
85
86
75
85
131K
86SWE-bench Verified 54.6% maps to 85 coding. GPQA 50.5% and MMLU 86% map to 80 logic. IFEval 92.1% yields 92 instruction. MATH 77% gives 85 math. 70B tier implies 75 speed. Text-only model.
DeepSeek: DeepSeek V4 Pro 0423$12.5368
98
91
70
88
1.0M
92SWE-bench Verified at 81.0% maps to 98 coding. GPQA Diamond at 66.3% maps to 92 logic. Flagship MoE tier justifies high scores; speed is standard for large MoE.
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).
Arcee AI: Trinity Large Thinking$18.0068
95
94
45
98
262K
95SWE-bench Verified at 63.2% maps to 95 coding. GPQA-Diamond at 76.3% maps to 95 logic. AIME 2025 at 96.3% maps to 98 math. As a heavy reasoning model (398B MoE), speed is mapped to 45.
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.
inclusionAI: Ring-2.6-1T$9.2567
88
86
60
90
262K
88Evidence claims SOTA on SWE-bench Verified and AIME26 but lacks exact percentages. As a 1T-parameter flagship MoE, coding and math are scored high (88-90). Speed is 60 based on 54.4 tokens/s throughput.
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.
Reflection: Beam 1.0 501B A23B$20.0067
95
93
45
98
1.0M
95SWE-bench Verified 80.9% maps to 95 coding. GPQA Diamond 90.5% maps to 96 logic. AIME 97.8% maps to 98 math. Heavyweight MoE reasoning model; speed mapped to 45.
Google: Gemma 3 4B$2.1067
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.
ByteDance Seed: Seed 1.6 Flash$6.0067
87
82
85
77
262K
82Benchable.ai cites Coding 87%, Reasoning 94%, Math 77%, and Instruction 70%, mapped directly to 0-100. As a Flash-tier model, it is optimized for speed (85) with native reasoning tokens, reflecting lightweight capabilities versus flagship models.
Xiaomi: MiMo-V2.5$7.1467
78
86
60
85
1.1M
84Pro-level agentic performance maps to MiMo-V2-Pro's SWE-bench Verified (78.0%) and GPQA Diamond (87.0%), yielding ~78 Coding and ~87 Logic. Speed reflects 29 tok/s. Multimodal is strong per omnimodal claims.
Qwen3.6 35B A3B$13.5067
92
90
85
90
262K
91SWE-bench Verified 73.4% maps to 92 coding. GPQA 86.0% maps to 92 logic. MMMU 81.7% maps to 90 multimodal. 3B active MoE architecture ensures high speed (85).
Qwen: Qwen3 Next 80B A3B Thinking$18.0067
90
93
45
97
262K
93GPQA at 77.2% maps to 96 logic. IFEval at 88.9% maps to 90 instruction. AIME 2025 at 87.8% maps to 97 math. As an 80B thinking model, speed is lower (45). No SWE-bench cited; coding estimated at 90.
Meta: Muse Spark 1.2 Contributor$6.0066
83
80
85
80
1.0M
81Terminal-Bench 2.1 at 82.9% maps to 83 coding. MMMU-Pro at 80.5% maps to 81 multimodal. As a cheaper 'Contributor' tier, speed is rated high (85), while logic and math are inferred around 80.

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Methodology: How we rank ROI / Value LLMs

Transparent, benchmark-driven rankings—same craft as our single-model deep dives.

Defining ROI on this leaderboard (quality per dollar)

ROI rankings combine normalized quality benchmarks with estimated monthly API spend for the same interactive workload. The goal is value density: which models deliver the most capability per dollar—helping growth, support, and product teams in the US, Canada, and Australia justify stack decisions with numbers, not hype.

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Value analysis

Benchmarks vs. estimated API cost—read the story your CFO cares about.

Interpreting ROI when your workload changes seasonally

ROI shifts when traffic spikes or when you enable vision and reasoning. Re-tune sliders to match launch vs. steady-state; if you bill clients in CAD or AUD, use relative rankings first, then apply your FX and tax reality. Many agencies embed this view so stakeholders see the same ROI story the technical team uses internally.

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How teams in the US, Canada, and Australia deploy these models in production.

Customer support bots, copilot features, and workflow automation

High-ROI models are the backbone of profitable AI features in B2B SaaS. Product teams in the US and Australia deploy these 'sweet spot' models for customer support chatbots, in-app writing copilots, and automated email drafting—use cases that require high reliability and instruction-following, but cannot justify the margin hit of a premium frontier model on every API call.

Architecture

Value-Driven Architecture

Strategies to reduce monthly API spend without sacrificing capability.

Blending models based on query complexity

Maximizing ROI requires dynamic routing. By using a fast, cheap model to classify user intent, you can route 80% of queries to a high-ROI mid-tier model, and only escalate the remaining 20% to an expensive reasoning model. This leaderboard helps you identify the perfect mid-tier anchor for your architecture, balancing acceptable latency with sustainable API margins.

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Frequently Asked Questions

Focused on teams in the United States, Canada, and Australia.

We pair composite quality signals with the monthly cost estimate from your token sliders. That highlights underpriced capability tiers—useful when agencies quote fixed-fee projects in USD, CAD, or AUD and need margin-friendly model choices.