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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 444 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.
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.
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).
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.
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: Qwen3.5-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.
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.
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.
Z.ai: GLM Flash Latest$5.5072
85
87
95
95
1.3M
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.
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.
NVIDIA: Nemotron 3 Super$7.4071
92
89
75
95
262K
91SWE-bench at 60.47% maps to 92 coding. GPQA at 79.23% maps to 92 logic. AIME25 at 90.21% yields 95 math. High scores reflect its 120B frontier reasoning capabilities.
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).
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.
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.
Gemma 4 31B$7.0069
78
89
45
95
262K
88HumanEval 76.8% maps to coding 78. MMLU 87.1% maps to logic 85. IFEval 93.7% maps to instruction 92. GSM8k 97.6% maps to math 95. Speed 8.52 t/s maps to 45. Native reasoning confirmed via 'reasoning_details'.
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.
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).
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.
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).
Qwen: Qwen3.5-9B$5.5068
70
87
85
83
262K
82GPQA Diamond 81.7% maps to 82 logic. IFEval 91.5% maps to 91 instruction. LiveCodeBench 65.6% maps to 70 coding. MMMU 78.4% maps to 78 multimodal. As a 9B lightweight model, speed is high (85).
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.
Qwen3.6 35B A3B$13.0067
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).
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.
Gemma 4 26B A4B$6.2067
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.
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.
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.
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.
DeepSeek V3.2$14.7666
92
89
45
95
164K
91SWE-bench at 67.8% maps to 92 for coding. MMLU-Pro 85.0% maps to 90 for logic. AIME 2025 at 89.3% yields 95 for math. As a frontier reasoning model, speed is 45.
Poolside: Laguna M.1$12.0066
96
87
70
85
262K
89SWE-bench Verified at 72.5% maps to 96/100 for coding. As a 225B flagship MoE with native reasoning tokens, logic and math are inferred high (88). Speed is estimated at 70 due to fp8 quantization and 23B active parameters.
OpenAI: GPT-5.6 Luna Pro$20.0066
92
93
45
95
1.1M
93Evidence lacks exact GPT-5.6 Luna Pro benchmarks. Inferred scores based on its 'Pro' heavyweight tier and native reasoning mode. Assigned high logic (95) and coding (92), with lower speed (45) typical for extended reasoning models.
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.
DeepSeek: DeepSeek V3.1 Terminus$20.8066
95
93
70
92
164K
93SWE Verified 68.4% maps to 95 coding. GPQA-Diamond 80.7% maps to 95 logic. Flagship tier model with native thinking mode; no vision or caching evidence found.
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.
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.

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Strategies to reduce monthly API spend without sacrificing capability.

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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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