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AI Agent Configurator

Konfigurišite svog AI agenta
sa pravim LLM-om

Svaki AI agent task traži drugačije snage - sirovu coding moć, autonomno CLI izvršavanje, duboko rasuđivanje ili maksimalan kontekst. Uskladite workload sa najboljim modelom kroz realne benchmark podatke, live procene trošda i preporuke po tipu zadatka.

12
Praćeni modeli
5
Benchmarkovi
8
Profili zadataka
Jun 2026
Ažurirano

Pronađite svoj AI model

Izaberite profil zadatka i pogledajte koji modeli imaju najviše rezultate prema ponderisanom benchmark matchingu. Podesite session parametre za procenu realnih troškova.

Hermes Agent

Hermes needs reliable tool calling, large context (memory + conversation), and prompt caching for long sessions. DeepSeek V4 Pro is the default.

Tool CallingPrompt CachingStructured OutputThinking Mode

Top Recommendations for Hermes Agent

1
DeepSeek

V4 Pro

1.6T MoE with 49B active per token. MIT open-source. 1M context with hybrid attention (CSA+HCA). State-of-the-art competitive coding and math reasoning at 7-9x lower cost than frontier models.

99%
Context
1.0M
SWE-Verified
80.6%
Terminal-Bench
67.9%
GPQA
90.1%
Est. Session Cost
$0.057
Price/MTok Out
$0.87
Open SourceThinking
2
MiniMax

MiniMax M3

Open-weight surprise. 92.68% GPQA Diamond, 80.5% SWE-bench Verified. 1M context. Very strong reasoning at $0.30-$0.60 input. Best value open-weight model.

98%
Context
1.0M
SWE-Verified
80.5%
Terminal-Bench
66.0%
GPQA
92.7%
Est. Session Cost
$0.099
Price/MTok Out
$1.80
Open SourceThinking
3
DeepSeek

V4 Flash

284B MoE with 13B active per token. MIT open-source. Ultra-cheap at $0.14/$0.28 per MTok. 1M context. Perfect for high-volume, cost-sensitive production workloads.

96%
Context
1.0M
SWE-Verified
79.0%
Terminal-Bench
56.9%
GPQA
88.1%
Est. Session Cost
$0.018
Price/MTok Out
$0.28
Open SourceThinking

All Models Ranked

RankModelScoreSession CostInput $Output $
1
V4 Pro
DeepSeek
99%$0.057$0.43$0.87
2
MiniMax M3
MiniMax
98%$0.099$0.45$1.80
3
V4 Flash
DeepSeek
96%$0.018$0.14$0.28
4
Sonnet 4.6
Anthropic
78%$0.900$3.00$15.00
5
Kimi K2.6
Moonshot AI
73%$0.260$1.00$4.00
6
Gemini 3.1 Pro
Google
72%$0.680$2.00$12.00
7
Opus 4.8
Anthropic
62%$1.500$5.00$25.00
8
GPT 5.3 Codex
OpenAI
60%$0.656$1.75$14.00
9
Haiku 4.5
Anthropic
54%$0.300$1.00$5.00
10
GPT-5.5
OpenAI
46%$1.575$5.00$30.00
11
GPT-5.4
OpenAI
36%$0.738$2.50$15.00
12
Fable 5
Anthropic
32%$3.000$10.00$50.00

AI model benchmark poređenje

Side-by-side poređenje svakog frontier modela kroz SWE-bench Verified, SWE-bench Pro, Terminal-Bench 2.0, GPQA Diamond i MMLU-Pro. Sortirajte po bilo kojoj koloni. Kliknite red za detalje.

Open
1.0M95.0%80.3%$10.00/$50.00
1.0M88.6%69.2%93.6%89.1%$5.00/$25.00
1.0M80.6%55.4%67.9%90.1%87.5%$0.43/$0.87
1.0M80.6%54.2%94.3%92.6%$2.00/$12.00
1.0M80.5%66.0%92.7%84.2%$0.45/$1.80
256K80.2%66.7%$1.00/$4.00
1.0M79.6%42.8%82.0%84.6%$3.00/$15.00
1.0M79.0%56.9%88.1%86.2%$0.14/$0.28
200K73.3%39.5%$1.00/$5.00
400K58.6%82.7%$5.00/$30.00
400K77.3%$1.75/$14.00
400K51.9%$2.50/$15.00

Data sourced from vendor reports, SWE-bench leaderboard, and third-party benchmarks as of June 17, 2026. Click a row for full details.

Cost Calculator

Izaberite dva modela i pogledajte koliko biste uštedeli po sesiji, mesečno i godišnje. Podesite turnove, tokene i učestalost sesija prema svom workloadu.

Input/MTok
$0.435
Output/MTok
$0.87
Per Session
$0.057
Monthly (30 sessions)
$1.70
Input/MTok
$5.000
Output/MTok
$25.00
Per Session
$1.500
Monthly (30 sessions)
$45.00
V4 Pro saves $43.30/month vs Opus 4.8
That's 96% cheaper — $520/year

Kako ocenjujemo modele

SWE-bench Verified

težina: 0-40%

Realni GitHub issue-i. Modeli popravljaju stvarne bugove u Python repoima pomoću standardizovanog scaffolda. Najbolji prediktor produkcione coding sposobnosti.

Terminal-Bench 2.0

težina: 0-40%

Autonomni CLI agent zadaci. Modeli navigiraju file systemima, pokreću buildove i orkestriraju shell komande - meri pravu autonomiju agenta.

GPQA Diamond

težina: 0-40%

Ekspertska naučna pitanja na PhD nivou iz biologije, fizike i hemije. Meri duboku sposobnost rasuđivanja izvan površinskog pattern matchinga.

MMLU-Pro

težina: 0-30%

Massive multitask language understanding - 57 oblasti kroz STEM, humanističke i društvene nauke. Širok benchmark znanja.

Podaci su iz SWE-bench leaderboard, vendor technical reports (DeepSeek, Anthropic, OpenAI), Artificial Analysis, and third-party evaluators. Artificial Analysis i third-party evaluatora. Poslednje ažuriranje 17. jun 2026. Cene proverene prema zvaničnoj API dokumentaciji.

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